{
 "cells": [
  {
   "cell_type": "markdown",
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     "start_time": "2021-01-23T12:18:22.606614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Create new features within a Pipeline\n",
    "\n",
    "In this notebook, I show how easy and practical is to create new features Feature-engine and the scikit-learn pipeline.\n",
    "\n",
    "For this demonstration, we use the UCI Wine Quality Dataset.\n",
    "\n",
    "The data is publicly available on [UCI repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)\n",
    "\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import feature_engine\n",
    "feature_engine.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:33.165436Z",
     "iopub.status.busy": "2021-01-23T12:18:33.164629Z",
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     "shell.execute_reply": "2021-01-23T12:18:34.763230Z"
    },
    "papermill": {
     "duration": 1.632752,
     "end_time": "2021-01-23T12:18:34.764012",
     "exception": false,
     "start_time": "2021-01-23T12:18:33.131260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "# import classes from Feature-engine\n",
    "from feature_engine.creation import MathematicalCombination, CombineWithReferenceFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:34.826210Z",
     "iopub.status.busy": "2021-01-23T12:18:34.825423Z",
     "iopub.status.idle": "2021-01-23T12:18:34.876223Z",
     "shell.execute_reply": "2021-01-23T12:18:34.875535Z"
    },
    "papermill": {
     "duration": 0.086863,
     "end_time": "2021-01-23T12:18:34.876336",
     "exception": false,
     "start_time": "2021-01-23T12:18:34.789473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset\n",
    "\n",
    "data = pd.read_csv('winequality-red.csv', sep=';')\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025681,
     "end_time": "2021-01-23T12:18:34.929009",
     "exception": false,
     "start_time": "2021-01-23T12:18:34.903328",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Let's have a look at the variables and their relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:34.991750Z",
     "iopub.status.busy": "2021-01-23T12:18:34.990639Z",
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     "shell.execute_reply": "2021-01-23T12:18:35.258089Z"
    },
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     "exception": false,
     "start_time": "2021-01-23T12:18:34.955529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check how many wines of different qualities there are\n",
    "\n",
    "# percentage of wines of each quality\n",
    "(data['quality'].value_counts() / len(data)).sort_index().plot.bar()\n",
    "\n",
    "# plot\n",
    "plt.title('Wine Quality')\n",
    "plt.ylabel('Percentage of wines in the data')\n",
    "plt.xlabel('Wine Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.027753,
     "end_time": "2021-01-23T12:18:35.315149",
     "exception": false,
     "start_time": "2021-01-23T12:18:35.287396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Most wines are medium to low quality. Only a few of high quality (>6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:35.391630Z",
     "iopub.status.busy": "2021-01-23T12:18:35.390786Z",
     "iopub.status.idle": "2021-01-23T12:18:35.507782Z",
     "shell.execute_reply": "2021-01-23T12:18:35.508352Z"
    },
    "papermill": {
     "duration": 0.164445,
     "end_time": "2021-01-23T12:18:35.508509",
     "exception": false,
     "start_time": "2021-01-23T12:18:35.344064",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's transform the target into binary\n",
    "\n",
    "# wines with quality below 6 will be considered low quality (0)\n",
    "data['quality'] = np.where(data['quality'] <= 6, 0, 1)\n",
    "\n",
    "(data['quality'].value_counts() / len(data)).plot.bar()\n",
    "\n",
    "plt.title('Wine Quality')\n",
    "plt.ylabel('Percentage of wines in the data')\n",
    "plt.xlabel('Wine Quality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:35.572752Z",
     "iopub.status.busy": "2021-01-23T12:18:35.571877Z",
     "iopub.status.idle": "2021-01-23T12:18:38.382007Z",
     "shell.execute_reply": "2021-01-23T12:18:38.380900Z"
    },
    "papermill": {
     "duration": 2.844211,
     "end_time": "2021-01-23T12:18:38.382235",
     "exception": false,
     "start_time": "2021-01-23T12:18:35.538024",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's explore variable distributions with histograms\n",
    "\n",
    "data.hist(bins=50, figsize=(10,10))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032299,
     "end_time": "2021-01-23T12:18:38.459251",
     "exception": false,
     "start_time": "2021-01-23T12:18:38.426952",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "All variables are continuous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:38.525363Z",
     "iopub.status.busy": "2021-01-23T12:18:38.524510Z",
     "iopub.status.idle": "2021-01-23T12:18:41.236003Z",
     "shell.execute_reply": "2021-01-23T12:18:41.236590Z"
    },
    "papermill": {
     "duration": 2.746418,
     "end_time": "2021-01-23T12:18:41.236743",
     "exception": false,
     "start_time": "2021-01-23T12:18:38.490325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x1980 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate the mean variable value per wine quality\n",
    "\n",
    "g = sns.PairGrid(data, x_vars=[\"quality\"], y_vars=data.columns[0:-1])\n",
    "g.map(sns.barplot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032536,
     "end_time": "2021-01-23T12:18:41.300609",
     "exception": false,
     "start_time": "2021-01-23T12:18:41.268073",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There doesn't seem to be a difference in pH between wines of low and high quality, but high quality wines tend to have more alcohol, for example.\n",
    "\n",
    "Similarly, good quality wines tend to have more sulphates but less free and total sulfur, a molecule that is part of the sulphates.\n",
    "\n",
    "Good quality wines tend to have more citric acid, yet surprisingly, the pH in good quality wines is not lower. So the pH must be equilibrated through something else, for example the sulphates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:41.366615Z",
     "iopub.status.busy": "2021-01-23T12:18:41.365894Z",
     "iopub.status.idle": "2021-01-23T12:18:42.395276Z",
     "shell.execute_reply": "2021-01-23T12:18:42.394684Z"
    },
    "papermill": {
     "duration": 1.063204,
     "end_time": "2021-01-23T12:18:42.395407",
     "exception": false,
     "start_time": "2021-01-23T12:18:41.332203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\fenotebook\\lib\\site-packages\\seaborn\\axisgrid.py:643: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vb3V4q2nuU77V6a2oukgQPvbYY1mHYNZQ3KfMys/9yqy83KfMys/9yqy83KeskdRFgtDMzMzMzMzMzMwqo2IJQklXSdoi6d6C5bMk/VLSfZL+rVKvn4Xe3l7OO+88ent7sw7FrGG4X5mVl/uUWfm5X5mVVy6X47TTTiOXy2UdilnD8L7KdqWSFYSLge78BZJOAk4Hjo6IVwKfr+DrV92SJUtYv349S5cuzToUs4bhfmXlVuoEVt7jkrRAUk7SOkmvqXaMleQ+ZVZ+CxYsYN26dSxcuDDrUMwawrx589i2bRvz5s3LOhSzhuHvgLYrFUsQRsQa4PGCxR8GLo2IZ9J1tlTq9autt7eXFStWEBGsWLHCWXmzMnC/sgpZTMEJrAKnAIelt5nAV6oQU1W4T5mVX29vL7fddhsAq1evdr8yG6VcLseGDRsA2LBhg6sIzcrA3wFtOKo9BuHLgRMk/UTSbZJeV2pFSTMlrZW0duvWrVUMcWSWLFnCjh07ANi+fbuz8mZl4H5llVDiBFa+04GlkbgTeJGkg6sTXWW5T5mV34IFCwa1XUVo5SKpW9Kv0or2T5VY592S7k+Hb/pWtWOshMKqQVcRmo2evwPacFQ7QdgGHAAcB3wSuE6Siq0YEYsiYnJETD7ooIOqGeOIrFq1ir6+PgD6+vpYuXJlxhE1Fo+X0Jzcrywj44GH89ob02U7qbeTWe5TZuXXXz3Yb/Xq1dkEYg1FUitwOUlV+xHAGZKOKFjnMOBC4I3p8E0frXacldBfPViqbWa7z98BbTiqnSDcCHw3rcr4KbADOLDKMVREV1cX/blOSUydOjXjiBqLx0toTl1dXbS1tQHQ1tbmfmU1p95OZrlPmZnVjWOBXEQ8GBHPAteQVLjn+3vg8oj4PTTO8E0dHR1Dts1s9/k7oA1HtROE3wdOApD0cmAP4LEqx1AR06ZNIyIAiAje/va3ZxxR4/B4Cc2rp6eHlpbk31RrayszZszIOCJrEpuAiXntCemyuuc+ZaNRbIIfSXMlbZJ0T3o7tcRzd3mpZL2aOHHikG2zERpONfvLgZdL+pGkOyUNNb5u3Zg9e/aQbTPbff4OaMNRsQShpG8DdwCvkLRR0lnAVcDL0i+W1wA90Z9Vq3Pf/OY3h2zbyHm8hObV3t5Od3c3kuju7qa9vT3rkKw5LANmpLMZHwf8MSIezTqocnCfslFaTPEJfv49Io5Jb8sLHxzOpZL1bM6cOUO2zSqojWRCrSnAGcDXJL2ocKV6Gw6js7NzoGqwo6ODzs7ObAMyawD+DmjDUclZjM+IiIMjYkxETIiIr0fEsxHx/oh4VUS8JiJ+UKnXr7bC8WcK2zZyzTBeQrGqjILHp0j6Y16FxkV5jzVsVQYkZ7uOPPJIn+Wysil2AkvSOZLOSVdZDjwI5ICvAf+QUagV4T5lIzWMCX5KGc6lknWrs7NzoGpw4sSJTmZYuQynmn0jsCwinouI3wC/JkkYDlJvw2FAUjU4duxYVw+aldGJJ56IJE488cSsQ7EaVe1LjBtWYSFkgxRG1oQmGS9hMcWrMvLdnlehcQk0flUGJGe7FixY4LNcVjYlTmBdERFXpI9HRHwkIg6NiCMjYm3WMZeT+5RVwLmS1qUnu/Yv8njDTvzTb86cOYwdO9bVg1ZOdwGHSTpE0h7AdJIK93zfJ6keRNKBJJccP1jFGCums7OTG2+80Ql3szK67LLL2LFjBwsXLsw6FKtRThCWyYQJE4Zs28g1w3gJrsowM7M69RXgUOAY4FHgC6PZWD1WOoGTGVZ+EdEHnAvcDDwAXBcR90m6RNK0dLWbgV5J9wM/BD4ZER6s28x2ksvlBmYE37BhA7lcLtuArCY5QVgmc+fOHbJtI9fe3s4b3vAGAI4//vhmrno5XtIvJN0k6ZXpsmFXZZiZmZVbRGyOiO0RsYPkkvxji6zWsBP/mFVSRCyPiJenFe3/ki67KCKWpfcjIj4WEUekFe/XZBuxmdWqefPmDdk2AycIy6azs3OganDChAk+g1xm/Wc4mvhMx93ASyPiaGAhySUlu6VeL9syM7PaJengvOZfA8XG0h3OpZJmZmZWIf3Vg6XaZuAEYVnNnTuXsWPHunqwzHK5HBs3bgRg48aNTZkkjIg/RcST6f3lwJh0rJlhV2XU62VbZmZWG4pN8AP8m6T1ktYBJwEXpOv+haTlUPpSyUzehJmZWRPqnxm8VNsMnCAsK48/UxkuhwZJL5Gk9P6xJH23F1dlmJlZlZSY4OcD6aWNR0XEtIh4NF33kYg4Ne+5O10qaWZmZtVx7rnnDmrPmjUro0isljlBaDWvGcqhi1VlSDpH0jnpKu8C7pX0C2ABMD0dd8ZVGWZmZhnr7e3lvPPOo7fX80OYmVntWbNmzZBtM3CC0OpAM5RDl6jKuCIirkgfvywiXhkRR0fEcRHx47znuirDzMwsQwsXLmTdunUsXLgw61DMzMx2smrVqkHtlStXZhSJ1TInCK3mzZ49e8i2mZmZWVZ6e3u57bbbALjttttcRWhmZjWnq6uLtrY2ANra2pg6dWrGEVktcoLQal5nZ+dA1WBHR4fHeDQzM7OasXDhQiICgIhwFaGZmdWcnp4eWlqS9E9rayszZszIOCKrRU4QlpHHn6mc2bNnM3bsWFcPmpmZWU3prx4s1TYzM8tae3s73d3dSKK7u5v29vasQ7Ia5ARhGS1ZsoT169ezdOnSrENpOJ4h2szMzGpRf/VgqbaZmVkt6Onp4cgjj3T1oJXkBGGZ9Pb2smLFCiKCFStWuIrQzMzMrAlMmDBhyLaZmVktaG9vZ8GCBa4etJKcICyTJUuWsGPHDgC2b9/uKkIzMzOzJvDRj350UPuCCy7IJhAzMzOzUXCCsExWrVpFX18fAH19fZ423KxMcrkcp512GrlcLutQzMzMdrJmzZoh22ZmZmb1oGIJQklXSdoi6d4ij31cUkg6sFKvX21dXV1IAkCSpw03K5N58+axbds25s2bl3UoZmZmO1m1atWgtk8Sm5nVPkkvknS9pF9KekDS8ZIOkLRS0v+mP/fPOk6zaqpkBeFioLtwoaSJwMnAbyv42lU3bdq0gUGpI4K3v/3tGUdkVv9yuRwbNmwAYMOGDa4iNDOzmuOTxGZmdWk+sCIiDgeOBh4APgXcGhGHAbembbOmUbEEYUSsAR4v8tC/A/8INNQUb8uWLRvUvuGGGzKKxKxxFFYNuorQzMxqjU8SW6VI6pb0K0k5STslKiSdKWmrpHvS24eyiNOs3kh6IXAi8HWAiHg2Iv4AnA4sSVdbArwji/jMslLVMQglnQ5siohfDGPdmZLWSlq7devWKkQ3OoWXk9xyyy0ZRWLWOPqrB0u1zczMsrZs2bJBFYQ+SWzlIKkVuBw4BTgCOEPSEUVWvTYijklvV1Y1SLP6dQiwFfiGpJ9LulLSWGBcRDyarvM7YFyxJ9dbrsJsuKqWIJS0N/D/gIuGs35ELIqIyREx+aCDDqpscGUwbty4Idtmtvs6OjqGbJuN1DCqMiZJ+mH6pXGdpFOziNPMat+qVasGVRB6DEIrk2OBXEQ8GBHPAteQVDeZ2ei1Aa8BvhIRrwa2UXA5cST/2Ite9VhvuQqz4apmBeGhJJn6X0jaAEwA7pb0kirGUDGPPvrokG0z232zZ88esm02EsOsypgNXJd+aZwOfLm6UZrVnmIT0En6XDrA+zpJ35P0ohLP3SBpfXoZ5NqqBV0FXV1dtLW1AdDW1uYxCK1cxgMP57U3pssKvTPtf9enY73vxNVOZjvZCGyMiJ+k7etJEoabJR0MkP7cklF8FdHb28t5551Hb29v1qFYjapagjAi1kfEiyOiIyI6SDrlayLid9WKoZLGjBkzZNvMdl9nZ+dA1WBHRwednZ3ZBmSNYjhVGQHsl95/IfBIFeMzq1WL2XkCupXAqyLiKODXwIVDPP+k9DLIyRWKLxM9PT20tCRfqVtbW5kxY0bGEVkTuQHoSPvfSp4fO20QVzuZDZbmIB6W9Ip00VuB+4FlQE+6rAf4rwzCq5glS5awfv16li5dmnUoVqMqliCU9G3gDuAVkjZKOqtSr1ULnnzyySHbZjYys2fPZuzYsa4etHIaTlXGXOD9kjYCy4FZxTbkqgxrJsUmoIuIWyKiL23eSXKFSFNpb2+nu7sbSXR3d9Pe3p51SNYYNgH5FYET0mUDIqI3Ip5Jm1cCr61SbGaNYBZwtaR1wDHAvwKXAlMl/S/QlbYbQm9vLytWrCAiWLFihasIrai2Sm04Is7YxeMdlXrtLIwdO5Zt27YNapsNl6SrgL8CtkTEq4o8/j7gnwABTwAf7p/sJ71k/wlgO9DXaJUZ+++/P4ceeij7779/1qFYczkDWBwRX5B0PPAfkl4VETvyV4qIRcAigMmTJxcdp6acFi5cSC6XG9U2Nm1Kji/Hjy92pdru6ezsZNasorlTa05/B1xb4rEAbpEUwFfTvrMTSTOBmQCTJk2qSJCV0NPTw4YNG1w9aOV0F3CYpENIEoPTgffmryDp4LwJFaYBD1Q3RLP6FRH3AMWOm95a5VCqYsmSJezYkXyN3b59O0uXLuWCCy7IOCqrNVWdxbiR5ScHi7XNdmExO1+2le83wJsj4kjgM6QJiTwNedkWuBTeKmKXVRnAWcB1ABFxB7AncGBVoquwp59+mqeffjrrMKzBSPo00AdcXWKVN0XEa0jG/vyIpBOLrVSvl0K2t7ezYMECVw9a2aSVuecCN5Mk/q6LiPskXSJpWrraeZLuk/QL4DzgzGyiLT+PlWZWXqtWraKvLyn47+vr84RaVlTFKgjN+tVSxUutVrtExBpJHUM8/uO8ZtNcwlVYCj9jxgwffFk57LIqA/gtyRnkxZL+kiRBmPk1xOX4/3X++ecDMH/+/FFvywxA0pkkVfBvjf7pfAtExKb05xZJ3yMZC3RN1YI0q0MRsZxkmIv8ZRfl3b+Qocf9rFuLFi1i3bp1LFq0iAsvbMi3aFZVXV1dLFu2bKDtCbWsGFcQWl1wxcsgZwE35bX7L9v6WXppVlH1OFZasVJ4s9EaZlXGx4G/T6syvg2cWSrxYdbMJHUD/whMi4inSqwzVtK+/feBk4F7i61rZtbb2ztQ3bRy5UpXEZqVwYknnjhk20aukSqeXUFYJpLIP3aUlGE0tcUVL+Uj6SSSBOGb8ha/KSI2SXoxsFLSL9OB5Aep9lhp5VCsFN5jZVg5DKMq437gjdWOy7LjavddSyegmwIcmE7gM4ekeukFJPsfgDsj4hxJfwFcGRGnAuOA76WPtwHfiogVGbwFM6sDixYtGjhBvGPHDlcRmpXBZZddNqi9cOFCFi9enE0wDSZ/SKx6P1Z1BWGZFBaWuNDEyk3SUSQz1J0eEQOnJ/Iv2wL6L9tqCF1dXbS0JP+mWlpaXApvZjWt0avdI+KMiDg4IsZExISI+HpEdEbExHQc3GMi4px03UfS5CAR8WBEHJ3eXhkR/5LtOzGzWnbrrbcO2Taz3bdhw4Yh2zYyvb293HTTTUQEN910U91XEbqC0KwOSJoEfBf4QET8Om/5WKAlIp7Iu2zrkozCLLuenp6BsTJ27Njh2SHNrGJc7W5mVhtceGFWfh0dHYOSgh0dHZnF0kiWLFkycMXbc889V/dVhK4gNKsB6WVbdwCvkLRR0lmSzpF0TrrKRUA78GVJ90hamy4fB/xPOk7aT4EbG+myrd///vdDts3MzMyssbz1rW8d1O7q6sooErPGMXv27CHbNjIrV64cOIkREdxyyy0ZRzQ6riA0qwERccYuHv8Q8KEiyx8Ejq5UXFmbN2/eTm2PlWFmZmbWuM4++2xWrVrFjh07aGlpYebMknPwmdkw/eEPfxjU/uMf/5hNIA1m3Lhxgyozx40bl10wZeAKQjOrWR4rw8zMzKy5tLe3D1QNTp06lfb29owjMqt/c+fOHdSeM2dONoE0mM2bNw/ZrjdOEJpZzSocG8NjZZiZWS3K5XKcdtppo54J28wSZ599NkcddZSrB83K5MknnxyybSMzdepUJAEgiZNPPjnjiEbHCUIzq1keK8PMzOrBvHnz2LZt205DY5iZmVnj6unpYcyYMQCMGTOm7ifVdILQzGrW/vvvP2TbzMwsa7lcbmAIjA0bNriK0KwMlixZwvr161m6dGnWoZiZldTe3k53dzeSOOWUU+p+SAQnCM2sZn31q18d1F60aFFGkZiZmRVXbEItMxu53t5ebrjhBiKCZcuW0dvbm3VIZmYl9fT0cOSRR9Z99SA4QWhmNezWW28d1F61alVGkZiZmRXnCbXMymvJkiVEBAAR4SpCM6tp7e3tLFiwoO6rB8EJQjOrYf0DvpZqm5mZZW3ixIlDts1s9yxfvnxQ+8Ybb8woErPG4ckfbTgqliCUdJWkLZLuzVv2OUm/lLRO0vckvahSr29m9e9Nb3rTkG0zM7OsvexlLxvUPvTQQzOKxKwx9PX1Ddk2s903derUQe3u7u6MIrFa1lbBbS8GLgPya8JXAhdGRJ+kzwIXAv9UwRiGbeHChWUfVPr8888f8XM7OzuZNWtWGaMxqz977LHHoPYLXvCCjCIxMzMr7q677hrU/ulPf5pRJNZoJHUD84FW4MqIuLTEeu8ErgdeFxFrqxiimdWJb3zjG4PaV155JdOnT88oGqtVFasgjIg1wOMFy26JiP5TQHcCEyr1+mZW/26//fZB7TVr1mQUiZmZWXFdXV20tCRfqVtaWnaq0jAbCUmtwOXAKcARwBmSjiiy3r7A+cBPqhuhmdUTV+bacFSygnBX/g64ttSDkmYCMwEmTZpU8WBGW633gx/8gEsuuWSgPWfOHE466aTRhmXW1MaNGzdosPdx48ZlF4yZmVkRPT09rFixgmeffZYxY8Y0xCyGVhOOBXIR8SCApGuA04H7C9b7DPBZ4JPVDc/M6omkgcl/+ttmhTKZpETSp4E+4OpS60TEooiYHBGTDzrooOoFN0JvectbBu63tbU5OWhWBo888siQbTMzq54S40sfIGmlpP9Nf+5f4rk96Tr/K6mnelFXXnt7O93d3Uiiu7u7IWYxtJowHng4r70xXTZA0muAiREx5CwekmZKWitp7datW8sfaZm1trYO2Taz3dfW1jZk20Yul8tx2mmnlX3IuixUPUEo6Uzgr4D3RX4KuwH0z1r36U9/OuNIzBrD9u3bh2ybmVlVLQYKRzX/FHBrRBwG3Jq2B5F0ADAHeD1JVdScUonEetXT08ORRx7p6kGrGkktwBeBj+9q3XorvCiclO6EE07IKBKzxjF+/Pgh2zZy8+bNY9u2bcybNy/rUEatqgnCdKDdfwSmRcRT1XztajjggAM4+uijXT1oViZOEJqZ1Y5i40uTXPK4JL2/BHhHkae+DVgZEY9HxO9JJq1rqOkT29vbWbBggasHrZw2ARPz2hPSZf32BV4FrJa0ATgOWCZpctUirJA999xzUNuT1JmN3u9+97sh2zYyuVxuYEisDRs21H0VYcUShJK+DdwBvELSRklnkcxqvC+wUtI9kq6o1Oub1ZNil20VPC5JCyTlJK1LLynpf6xhL9syqxRJ3ZJ+lfapnSqe0nXeLel+SfdJ+la1YzSrE+Mi4tH0/u+AYoPF7vJSyX71dimkWQXdBRwm6RBJewDTgWX9D0bEHyPiwIjoiIgOkgkgpzXCLMaFk9QVts1s973kJS8Zsm0jU1g1WO9VhJWcxfiMiDg4IsZExISI+HpEdEbExIg4Jr2dU6nXN6szixm6muIU4LD0NhP4CjTHZVtm5TacmSElHQZcCLwxIl4JfLTacZrVm3TomFENH1Nvl0KaVUpE9AHnAjcDDwDXRcR9ki6RNC3b6Cqrq6trUNszg5uN3ubNm4ds28jkT6hZrF1vMpmkxMwGK3HZVr7TgaWRuBN4kaSDaYLLtswqYGBmyIh4FuifGTLf3wOXp/2KiNhS5RjN6sXmdH9E+rNYX9nVpZJmVkRELI+Il0fEoRHxL+myiyJiWZF1pzRC9SDAiSeeOGTbzHbf1KlTB2YulsTJJ5+ccUSNoaOjY8h2vXGC0Kw+lLo8y5dtme2+4fSblwMvl/QjSXemY+ia2c6WAf3DW/QA/1VknZuBkyXtn1a5n5wuMzPbyWWXXTaovXDhwowiaUy9vb2cd9559Pb2Zh2KVVFPTw9jxowBYMyYMZ5Uq0xmz549ZLveeG5rsyYREYuARQCTJ0+u+AziCxcurMggreeff/6IntfZ2cmsWbPKHI01sDaSS/qnkFQ7rZF0ZET8IX8lSTNJLvtn0qRJVQ7RrLrS8aWnAAdK2kgyxMWlwHXpWNMPAe9O150MnBMRH4qIxyV9hmRMNYBLImKoqnkza2KNdslerVmyZAnr169n6dKlXHDBBVmHY8NQruOq/grCfffdl0suuWTE2/Fx1fM6OzvZZ599ePLJJ9lnn33o7OzMOqRRcQWhWX0odXlWQ1+2NXHixEFtJ2CsTIbTbzYCyyLiuYj4DfBrkoThIB4vzZpJifGleyPirRFxWER09Sf+ImJtRHwo77lXpWNRd0bEN7J7F2ZW6/bZZ58h2zZyvb29rFixgohgxYoVriJsMi0tLbS0tDBuXLH5xGwkent7eeaZZwB45pln6r5PuYLQrD4sA86VdA3JhCR/jIhHJd0M/GvexCQnk0yskLlynVWaMmUKAK2trSxdurQs27SmNzAzJElicDrw3oJ1vg+cAXxD0oEklxw/WM0gzax+9Pb2cvHFFzNnzhza29uzDsesrvX19Q3ZtpFbsmQJO3bsAGD79u2uIqwT5Tqu6r8Sa/78+WXZniV9KpmjDSKi7vvULisIJY2T9HVJN6XtI9LLSMysTNLLtu4AXiFpo6SzJJ0jqX+m7+UkyYkc8DXgHwDSSo3+y7buogEv2+qvIqz38RxqTTOPPzPMmSFvBnol3Q/8EPhkRDTfh2Vmw5J/yZ6VTzPvq5pZ4eQJb3vb2zKKpPGsWrVqIOHa19fHypUrM44oW5JaJf1c0n+n7UMk/URSTtK1kvbIOkarbY3Wp4ZzifFikgOlv0jbvwY+WqF4zJpSicu2roiIK9LHIyI+ks5id2T+LHWNftnWAQccwNFHH81JJ52UdSgNpdkPZnc1M2Ta5z4WEUekfe6abCM2s1rV29vL8uXLiQiWL1/uZFYZNfu+qll5FuPK6erqoq0tuYiwra2NqVOnZhxR5s4nOVnc77PAv0dEJ/B7wIVRNqRG61PDSRAeGBHXATtgoPJie0WjMjOzivH4M2Zm5bNkyZKB6oHnnnvOyawy8b6qeX3pS18asm0j19PTQ0tLkgJobW1t6plsJU0ATgOuTNsC3gJcn66yBHhHJsFZ3Wi0PjWcBOE2Se1AAEg6DvhjRaMyM7OKKTb+jJmZjcwtt9wyqH3zzTdnFElj8b6qeW3cuHFQ++GHH84oksbT3t5Od3c3kuju7m72MVO/BPwjaSEU0A78IS2IgmTCuvHFnihppqS1ktZu3bq14oFa7Wpvb+cNb3gDAMcff3zd96nhJAg/RjJBwqGSfgQsBTyntZlZnWq0sTLMzLLU2to6ZNtGxvsqs8ro6enhyCOPrPtKp9GQ9FfAloj42UieHxGLImJyREw+6KCDyhyd1Zv7779/0M96tssEYUTcDbwZeANwNvDKiFhX6cDMzKwyGm2sDDOzLG3btm3Ito1MV1fXwGVbLS0t3lc1kf7fe6m2jU57ezsLFiyo+0qnUXojME3SBuAakkuL5wMvktSWrjMB2JRNeFYvcrkcW7ZsAWDLli3kcrmMIxqd4cxiPAN4L/Ba4DXAGekyMzOrQ/ljZbS0tDT1GWQzM6tNPT09A5cY79ixw/uqJtLV1TWo7eSwlVtEXJhODNkBTAd+EBHvA34IvCtdrQf4r4xCtDrxz//8z0O2681wTse8Lu92AjAXmFbBmMzMrILa29vZf//9Adh///2b/QyymdmoHH/88UO2bWR+//vfD9m2xnX22WeTzBcBkpg5c2bGEVkT+SfgY5JyJGMSfj3jeKzGPfroo0O2681wLjGelXf7e5Iqwn0qH5qZmVVCb28vmzdvBmDz5s2eGdLMbBT23XffQe399tsvo0gay7x584ZsW+Nqb28fqBo8+eSTfSLTKioiVkfEX6X3H4yIYyOiMyL+NiKeyTo+s2oayYAO24BDyh2ImZlVx+c///kh22ZmNnxr1qwZ1L7tttsyiqSxbNiwYci2Nbazzz6bo446ytWDZlbT+qudS7XrTduuVpB0AxBpswU4ArhuGM+7CuifHehV6bIDgGuBDmAD8O6I8PUCZmZVdMcddwzZNqsnCxcurJkBofvjOP/88zOOJNHZ2cmsWbOyDqPh9U/6VKptI9PR0TEoKdjR0ZFZLFmR1E0ycUIrcGVEXFrw+DnAR4DtwJPAzIio/2k0eX4iDTOzShrt98h9992XP/3pTwPt/fbbb1TfA7P+7jacbzD5pSV9wEMRsXEYz1sMXAYszVv2KeDWiLhU0qfS9j8NM1arMh90lZZ1xzUzs0Qul+Oeex9g+94HZB0KLc8m51N/9uDmjCOB1qcezzqEpvHkk08O2baRmT17Nh/60IcGtZuJpFbgcmAqsBG4S9KyggTgtyLiinT9acAXge6qB2tm1qQmTJjA/fc//295/PjxGUYzertMEEbEiK6TiIg1kjoKFp8OTEnvLwFW4wRhzfJBV3E+6LJ619bWRl9f36C2WT3bvvcBPH34qVmHUVP2+uXyrEMwG5XOzs6BKsKOjg46OzuzDqnajgVyEfEggKRrSI6lBo5EI+JPeeuP5fmrvszMbBjKUfQzbdo0/vSnPzFlyhTmzp07+qAyVPKoUNITFN/JCIiIGMkIzOMion9al98B44Z4/ZnATIBJkyaN4KWsHHzQtTMfdFm9a21tHZQgbG1tzTAaM6t3kl5BMoRMv5cBF0XEl/LWmQL8F/CbdNF3I+KSKoVodWr27Nmcf/75TVc9mBoPPJzX3gi8vnAlSR8BPgbsAbyl2IZ8XGVmVjkTJkzgoYceaogrDEtOUhIR+0bEfkVu+44wOVi4/WCIs1wRsSgiJkfE5IMOOmi0L2dmZqm3ve1tg9rd3b4aycxGLiJ+FRHHRMQxwGuBp4DvFVn19v71nBy04ejs7OTGG29sxurBYYuIyyPiUJKrsopmUn1cZWZWOWPGjKGzs7MhZlwf9izGkl4saVL/bYSvt1nSwen2Dga2jHA7ZmY2Qj09PQOXFbe1tTFjxoyMIzKzBvJW4P8i4qGsA6mWAw88cFDbCRgrk03AxLz2hHRZKdcA76hkQGZm1th2mSCUNE3S/5JcEnIbyezDN43w9ZYBPen9HpJLTcyMZKY6Sb+SlEsn8Sl8/N8l3ZPefi3pD3mPbc97bFlVA7e6097ezqmnnookTjvttIY422VmNWM68O0Sjx0v6ReSbpL0ymoGVUmHHXbYoLar3axM7gIOk3SIpD1I+tag73iS8v/4TgP+t4rxVVQul+O0006rmQkTzcyawXAqCD8DHAf8OiIOITkzfOeuniTp28AdwCskbZR0FnApMDVNOHalbbOmlzdT3SnAEcAZko7IXyciLsi7hGsh8N28h5/Ou2xrWrXitvrV09PDkUce6epBMyubNIkxDfhOkYfvBl4aEUeT7MO+X2IbMyWtlbR269atFYu1nH76058O2TYbiYjoA84FbgYeAK6LiPskXZLOWAxwrqT7JN1DMg5hT/Gt1Z+5c+eybds2Lr744qxDMTNrGsOZuvK5iOiV1CKpJSJ+KOlLu3pSRJxR4qG37laEZs1hlzPVFTgDmFOl2KwBtbe3s2DBgqzDMLPGcgpwd0RsLnwgf7bViFgu6cuSDoyIxwrWWwQsApg8eXJdzMi6ffv2IdtmIxURy4HlBcsuyrt/ftWDqoJcLsfGjRsBePjhh8nlcq7MNTOrguFUEP5B0j7A7cDVkuYD2yobllnTKTZT3fhiK0p6KXAI8IO8xXumFRd3SnpHiefVXVWGmZnVlTMocXmxpJdIUnr/WJLvoL1VjK1iWlpahmyb2e6ZO3fuoLarCM3MqmM4FYQ/BF4InA+8P73vmefMsjMduD4i8ksUXhoRmyS9DPiBpPUR8X/5T6rHqgwrbuHChaMek2fTpmSc8/Hji+ahd0tnZyezZs0a9XaqSVI3MB9oBa6MiKJDXkh6J3A98LqIWFvFEM3qiqSxwFTg7Lxl5wBExBXAu4APS+oDngamR0RD7IvGjx/Pww8/PKht5dHb28vFF1/MnDlzPF5uE+mvHuyX37/MzKxyhnOKsw24BVgN7AtcGxENccbXrIbszkx1Ow0AHxGb0p8PkvTVV5c/RGskTz/9NE8//XTWYWRiOGN+puvtS3Jy7CfVjdCs/kTEtohoj4g/5i27Ik0OEhGXRcQrI+LoiDguIn6cXbTl9dhjjw3ZtpFbsGAB69atY+HChVmHYlWUFhuXbJuZWWXssoIwIi4GLpZ0FPAe4DZJGyOiq+LRmTWPgZnqSBKD04H3Fq4k6XBgf5IJgPqX7Q88FRHPSDoQeCPwb1WJ2jJRjmq9889Phi2aP3/+qLdVh4Y75udngM8Cn6xueGZWT0444QRuueWWgfaJJ56YYTSNo7e3l9tuuw2A1atX09vb6yrCJnHcccdxxx13DGqbmVnlDecS435bgN+RjBfz4sqEMzLluNyuHPpj6D/wzlo9XvbXrCKiT1L/THWtwFX9M9UBayNiWbrqdOCagsuy/hL4qqQdJFXBl0ZEqclNzKz4mJ+vz19B0muAiRFxo6SyJAi9ryputPuqTZs20frUH9nrl8t3vXITaX2ql02b+rIOoyk88cQTQ7ZtZAon0lq4cOFOY9NZY9pvv/2GbJuZWWXsMkEo6R+AdwMHAd8B/r7Wkg+5XI577n2A7XsfkGkcLc8mOZufPbjT5H1V1/rU41mHYLtpVzPVpe25RZ73Y+DIigZn1kQktQBfBM4cxrozgZkAkyZNGnJd76t25n2VNYI777xzUDu/8slGrr96sN/q1auzCcSqrvB3f9ttt3HhhRdmFI2ZWfMYTgXhROCjEXFPhWMZle17H8DTh5+adRg1w5UUZmYl7WrMz32BVwGr03GPXgIskzStcKKS3Z38x/uqwcqxrxo/fjy/e6bNn2uBvX65nPHjx2UdRlMonGulQeZeMcuMxyA0M8vGcMYg9OkaMzNrJEOO+ZlOsnBgf1vSauATnsXYzIppaWlhx44dg9o2emPHjmXbtm2D2tYcCidRa9ZJ1czMqs3fYMzMrKlERB/QP+bnA8B1/WN+SpqWbXRmVm/yk4PF2jYyzz333JBtMzMzK6/dmaTEmowHfi+uGQZ+r5XJFKC2JlTwxD+NYzhjfuYtn1KNmMzM7Hl77LEHzz777KC2mY1eb28vF198MXPmzPHM4GY2iBOEZraTWplMAWpnQgVPpmBmZlY9Tz755JBtMxuZJUuWsH79epYuXcoFF1yQdThmVkOcILSSPPB7cc0y8LsnUxjMlbRmZmZmVs96e3tZsWIFEcGKFSuYMWOGqwjNbIDHIDQzMzMzMzNrcEuWLBkYJ3X79u0sXbo044jMrJY4QWhmZmZmZlZjJHVL+pWknKRPFXn8Y5Lul7RO0q2SXppFnFY/Vq1aRV9fMpZ6X18fK1euzDgiM6slThCamZmZmZnVEEmtwOXAKcARwBmSjihY7efA5Ig4Crge+LfqRmn1pquri7a2ZJSxtrY2pk6dmnFEZlZLMkkQSrpA0n2S7pX0bUl7ZhGHmZmZmZlZDToWyEXEgxHxLHANcHr+ChHxw4h4Km3eCUyocoxWZ3p6emhpSVIAra2tzJgxI+OIzKyWVD1BKGk8cB7J2a5XAa3A9GrHYWZmZmZmtUnSkO0mMB54OK+9MV1WylnATcUekDRT0lpJa7du3VrGEK3etLe3093djSS6u7s9QYmZDZLVLMZtwF6SngP2Bh7JKA4zMzOrc61PPV4TM423/PlPAOzYc7+MI0k+ExiXdRhmI3bcccdxxx13DLSPP/74DKOpbZLeD0wG3lzs8YhYBCwCmDx5clQxtBF58YtfzJYtWwba48b5f1k59fT0sGHDBlcPmtlOqp4gjIhNkj4P/BZ4GrglIm4pXE/STGAmwKRJk6obpJmZmdWFzs7OrEMYkMs9AUDny2rhYHZcVT8bSRuAJ4DtQF9ETC54XMB84FTgKeDMiLi7agFa3dl3332HbDeBTcDEvPaEdNkgkrqATwNvjohnqhRbRfX29g5qP/bYYxlF0pja29tZsGBB1mE0jYULF5LL5bIOA2AgjvPPPz/jSJLvb7Nmzco6DCtQ9QShpP1Jxs84BPgD8B1J74+Ib+avV29nuswayaZNm2h96o81UZFTK1qf6mXTpr6swzCzArX05bL/C/f8+fMzjiQzJ0VEqSP5U4DD0tvrga+kP82Kuv322we116xZw4UXXphRNJm4CzhM0iEkicHpwHvzV5D0auCrQHdEbNl5E/Vp+/btQ7bNRkvSRGApSal9AIsiYr6kA4BrgQ5gA/DuiPj9aF4rl8txz70PsH3vA0YXdBm0PJukVX724OZM40iucrBalMUlxl3AbyJiK4Ck7wJvAL455LOG4GTGzpzMqD+SukmqK1qBKyPi0oLHzwQ+x/Nnjy+LiCvTx3qA2enyeRGxpCpBm5mZDc/pwNKICOBOSS+SdHBEPJp1YFabxo0bx4YNGwa1m0lE9Ek6F7iZ5LvhVRFxn6RLgLURsYzke+E+JAUXAL+NiGmZBW1WP/qAj0fE3ZL2BX4maSVwJnBrRFwq6VPAp4B/Gu2Lbd/7AJ4+/NTRbqZhOG9Tu7JIEP4WOE7S3iSXGL8VWJtBHGY1Q1IrcDkwlWQQ6rskLYuI+wtWvTYizi147gHAHJKxZ4JkB7dsNGe7xo8fz++eafOOLM9ev1zO+PHNdXBiZrYbArhFUgBfTa8EyVdqwoVBCUIPMWP9Nm/ePGS7GUTEcmB5wbKL8u53VT0oswaQnpx6NL3/hKQHSPZJpwNT0tWWAKspQ4LQrF5kMQbhTyRdD9xNkrn/OemlxCPlZMbOnMyoO8cCuYh4EEDSNSQ7qMIEYTFvA1ZGxOPpc1cC3cC3KxSrmZlZoTel40y/GFgp6ZcRsWZ3N1KPQ8yMGTOG5557blDbRu+EE07gllueH6b8xBNPzDAaM2tUkjqAVwM/AcblVbb/Ds/2ZU2mJYsXjYg5EXF4RLwqIj7QKAPqmo1CqcqKQu+UtE7S9enYGbvzXDMzs4qIiE3pzy3A90hOfOUb1oQL9Sg/OVisbSPzxBNPDNk2MxstSfsA/wl8NCL+lP9YOiRG0RNVkmZKWitp7datW6sQqVl1ZHGJsdWR1qcer4kxAlr+nPy/3rHnfhlH0j+oaiYnk24Avh0Rz0g6m6Ts/S3DfbIv26oNtTKTWS3NYgaeycysnkkaC7Skl2mNBU4GLilYbRlwbloh/3rgjx5/0IZy5513DmrfcccdGUViZo1I0hiS5ODVEfHddPHm/vFxJR0MFJ38px6r3c2GwwlCK6mzszPrEAbkcslZ486X1UKV97hKfDa7rKyIiN685pXAv+U9d0rBc1cXvoB3ZLWhVmYyq5VZzKB5ZjLzhFo784RaDWMc8L10koQ24FsRsULSOQARcQXJOGqnAjngKeCDGcVqdSIp3indNrOR6e3t5eKLL2bOnDm0t7dnHU4mlOywvg48EBFfzHtoGdADXJr+/K8MwrNhcuFFcaMpvHCC0EqqpWqe/s42f/78jCOpmLuAwyQdQpLwmw68N3+FgtkepwEPpPdvBv5V0v5p+2TgwsqHbCPlmcwGc8LMrL6l4+ceXWT5FXn3A/hINeMyM7OdLVmyhPXr17N06VIuuOCCrMPJyhuBDwDrJd2TLvt/JInB6ySdBTwEvDub8Gw4XHixs9EWXjhBaFYDIqJP0rkkyb5W4KqIuE/SJcDaiFgGnCdpGsnkPo8DZ6bPfVzSZ0iSjACX9E9YYma1wxNq7cwTaplZKS9+8YvZsmXLoLaZjU5vby8rVqwgIlixYgUzZsxoyirCiPgfQCUefms1Y7HRceHFYKMtvGiYBGEtjJXncfJsNCJiOcklWPnLLsq7fyElKgMj4irgqooGaGZmZlYlhQP/eyIAs9FbsmQJO3bsAGD79u3NXkVoZgUaIkFYK2PlNcE4eWZmDUFSNzCfpGL3yoi4tODxjwEfIqnY3Qr8XUQ8VPVAzcyalMcgNCu/VatW0deXjP3b19fHypUrnSA0swENkSCslbHymmCcPGsitVCVC7VTmeuq3MYhqRW4HJgKbATukrQsIu7PW+3nwOSIeErSh0kmBXpP9aM1MzNrLpIGJYTTCZCsDLq6urjhhhuICCQxderUrEMysxrSEAlCMyuvWqo+rZ3KXFflNpBjgVw6sQKSrgFOBwYShBHxw7z17wTeX9UIzcya3NixY9m2bdugtjUHV49WzrRp01i2bBmQfK5vf/vbM47IzGqJE4RmtpNaqcoFV+ZaRYwHHs5rbwReP8T6ZwE3FXtA0kxgJsCkSZPKFZ+ZWd1buHAhuVxuxM/PTw72t/u/E4xEZ2dnTX2/McvCN7/5zZ3ac+fOzSYYM6s5ThCamZmVIOn9wGTgzcUej4hFwCKAyZMnu8TBrM6MNolVykgTWU5iPa+9vZ3e3t5BbasPlehX7lPlsXr16iHbVn6bNm2i9ak/1sTQTbWi9aleNm3qyzoMK8IJQjMzazabgIl57QnpskEkdQGfBt4cEc9UKTYzs4Yw2qRMb28v73znOwEYM2YMixYtcpLQmppPaJhZpTlBaGZWRT6LuLMMziLeBRwm6RCSxOB04L35K0h6NfBVoDsitlQzODOrnnIc3K5du5ZPfOITA+0vfOELvPa1rx31dptde3v7QBXhqaee6uRgHRltv5oyZcpOyzzUjNWr8ePH87tn2nj68FOzDqVm7PXL5Ywfn/X48laME4RmZtZUIqJP0rnAzUArcFVE3CfpEmBtRCwDPgfsA3wnnT3xtxExLbOgzaxmTZ48eeD+mDFjnBwso3HjxvHnP/+ZGTNmZB1KJiR1A/NJ9lVXRsSlBY+fCHwJOAqYHhHXVz3ICujs7BxUKXf44YdnGE3tKMcJjblz5w66rHjKlCkeg9DMBjhBaGZWRT6LuLMsziJGxHJgecGyi/Lud1U1IDOra4cccgi/+c1vuPTSS3e9sg3bmDFj6OzsbMrqQUmtwOXAVJLJtO6StCwi7s9b7bfAmcAndt5C/bryyisHVRFeccUV2QXTYGbNmjWQIJTkS4TNbJCWrAMwMzMzM6tn++23H0cffbSrB62cjgVyEfFgRDwLXAOcnr9CRGyIiHXAjiwCrKQXvOAFgKsHy629vZ399tsPgDe/+c1NmXw3s9IyqSCU9CLgSuBVQAB/FxF3ZBGLmZmZmZlZjRkPPJzX3gi8fiQbkjQTmAkwadKk0UdWBf2JQY89WH4TJkzgoYcecvWg1T2P7b6z0Y7tntUlxvOBFRHxLkl7AHtnFIeZmZmZmVnDiohFwCKAyZMnR6n1KjVL7kj0xzHSGXbLrZFm7G3mS/fNbGhVTxBKeiFwIsl4GaQl889WOw4zs6y0PvV45me6Wv78JwB27LlfpnFA8nmAZzIzq1eSJgJLSTpyAIsiYn7BOlOA/wJ+ky76bkRcUsUwbTfUSqKoyZNEm4CJee0J6bKKyeVy3HPvA2zf+4BKvsywtDyb5DF/9uDmjCPp/54yOrXSp6C2+lUjJV6t+jy2+85GO7Z7FhWEhwBbgW9IOhr4GXB+RGzLX6keS+HNzHals7Mz6xAAyOWeAKDzZbWQmBtXM5+LmY1IH/DxiLhb0r7AzyStLJhMAeD2iPircr2oD7iLK8cBd60kihotSbSb7gIOk3QISWJwOvDeSr/o9r0P8MF2gXKc1K2VPgW1068y6FNmtgtZJAjbgNcAsyLiJ5LmA58C/jl/peGWwpuZ1ZNaOUvafxDrsX3MbLQi4lHg0fT+E5IeIBk/rTBBWFY+4N5ZOQ+4nSgarNqV/xHRJ+lc4GagFbgqIu6TdAmwNiKWSXod8D1gf+Dtki6OiFeO9DU9nldxox3Tq5/71GD+OzOrPVkkCDcCGyPiJ2n7epIEoVlTk9RNMj5nK3BlRFxa8PjHgA+RVGpsJZnc56H0se3A+nTV30bEtKoFbmZmlpLUAbwa+EmRh4+X9AvgEeATEXFfkefv1hUkPuAezAfcjSUilgPLC5ZdlHf/LpJLj63GOfm6s3IlXs2sfKqeIIyI30l6WNIrIuJXwFup8Blms1onqRW4HJhKkkS/S9Kygsuzfg5MjoinJH0Y+DfgPeljT0fEMdWM2czMLJ+kfYD/BD4aEX8qePhu4KUR8aSkU4HvA4cVbsNXkNQGJzN21gzJjPHjx7P194VdNxu1NFYyiPHjx2cdhNWxWhh/HGqnX3n88dqV1SzGs4Cr0xmMHwQ+mFEcZrXiWCAXEQ8CSLoGOJ285HlE/DBv/TuB91c1QjMzsxIkjSFJDl4dEd8tfDw/YRgRyyV9WdKBEfHYSF/TSaydNUMSyyqnlsYDbrSxkj2Zws5GO5lCvXC/Ksbjj9eqTBKEEXEPMDmL1zarUeOBh/PaG4HXD7H+WcBNee09Ja0lufz40oj4fuETPPGPWfZq4QxyrZw9Bp9BbhSSBHwdeCAivlhinZcAmyMiJB0LtAC9VQzTdoOTGTtrhmRGrYyTDI05VnItfAeA2vke0CzfAdyvrJ5kVUFoZiMk6f0kCfY35y1+aURskvQy4AeS1kfE/+U/z5dtmWWrVs6U1s7ZY/AZ5IbxRuADwHpJ96TL/h8wCSAirgDeBXxYUh/wNDA9Ika1L3ISa2flTGLVQjKjVhIZ0DzJDKuMWtrX1c73AH8HsNHzvmqw0e6rnCA0qw2bgIl57QnpskEkdQGfBt4cEc/0L4+ITenPByWtJhkg/v8Kn29m2amVM8g+e2zlFhH/A2gX61wGXFbu166FAwOonYODciWxauWgvXYSGeBkRnVt3bqVRx55hBtuuIG3v/3tWYczarXyHQD8PcAaR638T26kfZUThGa14S7gMEmHkCQGpwPvzV9B0quBrwLdEbElb/n+wFMR8YykA0kqOf6tapGbmZlloFYODKCWDg7Kk8SqlWSGExnN65FHHgHgi1/8YkMkCM2s/LyvKj8nCM1qQET0SToXuBloBa6KiPskXQKsjYhlwOeAfYDvJMM98duImAb8JfBVSTtIxnS6tGD2YzMzs4ZTKwcG0FgHB2ajtXDhQnK53Iifv3Xr1oH7EcH73vc+DjzwwBFtq7Ozs6b+V5iZ1TInCM1qREQsB5YXLLso735Xief9GDiystGZmZmZmVVef/Vgv02bNo04QdhIRpt47de/jf4TGyPl5KtZ43GC0MzMzMzMzMpitEmjKVOm7LTM1bnls9dee2UdgpnVKCcIreLKcbbLZ7rqj89ymlmzabRB9c3MrHb4e6xZ+dVSrgKyP2Z1gtDqgs90NS//7s2sGsrxBbH/srgvfOELrFq1asTbyfrLoVk5+KDLRuqoo45i3bp1g9pWPrlcjvPPP5/58+fX1GRPZvWqkY5XnSC0ivOXsebk37vVMkndwHySSYGujIhLCx5/AbAUeC3QC7wnIjZUO06rH/mD6gM89thjHjPLbJQa6aDLhm/OnDm8853vHNS28pk3bx7btm1j3rx5LF68OOtwzDLlY9bBnCA0M7OmIqkVuByYCmwE7pK0rGD277OA30dEp6TpwGeB91Q/WquWco+ZtWnTJq6++upRbdOsnvmgy0aqvb19oIrwqKOOor29PeuQGkYul2PDhg0AbNiwgVwu5ypCMxvgBGHKl0GYWb3w/6tROxbIRcSDAJKuAU4H8hOEpwNz0/vXA5dJUkRENQMt5N+9WXl5vFyz2jRnzhwuvvhiVw+W2bx583Zqu4pwZ7u60qTavK+yamnJOoBGstdee/lSCDOrC03+/2o88HBee2O6rOg6EdEH/BHYqYRB0kxJayWtLbzEtFY1+e/erCLcr6wSJHVL+pWknKRPFXn8BZKuTR//iaSODMKsiPb2dhYsWODqwTLrrx4s1bZBV5qcAhwBnCHpiGyjKg/vq2xXXEGYcgbczOqF/1/VjohYBCwCmDx5csWrC/27Nysv9ymrVR4Owyqho6NjUFKwo6Mjs1hq2HCuNKkq76usWlxBaGZmzWYTMDGvPSFdVnQdSW3AC0kmKzEr6m/+5m8Gtf/2b/82o0jMrEEMJCki4lmgP0mR73RgSXr/euCtklTFGK3OzJ49e8i2AcO40qQeryAxGw4nCM3MrNncBRwm6RBJewDTgWUF6ywDetL77wJ+kPX4g1bbzjvvvEHtj3zkIxlFYmYNoqmHw7DK6OzsHKga7Ojo8AQlIxQRiyJickRMPuigg7IOx6xsnCA0M7Omkh5EnQvcDDwAXBcR90m6RNK0dLWvA+2ScsDHgJ3GfjIr1F9F6OpBM6slTmZYvtmzZzN27FhXD5Y2nCtNzBqSxyA0M7OmExHLgeUFyy7Ku/9nwFke2y3nnXfeTpWEZmYjtDvDYWz0cBg2XJ2dndx4441Zh1HLBq40Ielj04H3ZhuSWXW4gtDMzMzMzKy2eDgMswyUutIk26jMqkP1sA+RtBV4KOs4hulA4LGsg2hQ9fTZPhYR3VkHUUqd9Smor999Pam3z9X9qnzq7XdfT+rps3WfKq96+t3Xk3r7XMvWrySdCnwJaAWuioh/kXQJsDYilknaE/gP4NXA48D0/plXh9hmPfWrevvd15N6+my9ryqvevrd15N6+1yL9qu6SBDWE0lrI2Jy1nE0In+2zcu/+8rw59q8/LuvHH+2zcu/+8rw59q8/LuvHH+2zcu/+8polM/VlxibmZmZmZmZmZk1MScIzczMzMzMzMzMmpgThOW3KOsAGpg/2+bl331l+HNtXv7dV44/2+bl331l+HNtXv7dV44/2+bl331lNMTn6jEIzczMzMzMzMzMmpgrCM3MzMzMzMzMzJqYE4RmZmZmZmZmZmZNzAnCMpLULelXknKSPpV1PI1C0lWStki6N+tYrLrcpyrDfaq5uV9VhvtV83Kfqgz3qebmflUZ7lfNy32qMhqtTzlBWCaSWoHLgVOAI4AzJB2RbVQNYzHQnXUQVl3uUxW1GPeppuR+VVGLcb9qOu5TFbUY96mm5H5VUYtxv2o67lMVtZgG6lNOEJbPsUAuIh6MiGeBa4DTM46pIUTEGuDxrOOwqnOfqhD3qabmflUh7ldNy32qQtynmpr7VYW4XzUt96kKabQ+5QRh+YwHHs5rb0yXmdnIuE+ZlZ/7lVl5uU+ZlZ/7lVl5uU/ZsDhBaGZmZmZmZmZm1sScICyfTcDEvPaEdJmZjYz7lFn5uV+ZlZf7lFn5uV+ZlZf7lA2LE4TlcxdwmKRDJO0BTAeWZRyTWT1znzIrP/crs/JynzIrP/crs/Jyn7JhcYKwTCKiDzgXuBl4ALguIu7LNqrGIOnbwB3AKyRtlHRW1jFZ5blPVY77VPNyv6oc96vm5D5VOe5Tzcv9qnLcr5qT+1TlNFqfUkRkHYOZmZmZmZmZmZllxBWEZmZmZmZmZmZmTcwJQjMzMzMzMzMzsybmBKGZmZmZmZmZmVkTc4LQzMzMzMzMzMysiTlBaGZmZmZmZmZm1sScIDQkdUi6N70/WdKC9P4USW/INjqz+uM+ZVZ+7ldm5eU+ZVZ+7ldm5eU+VV1tWQdgtSUi1gJr0+YU4Engx5kFZFbn3KfMys/9yqy83KfMys/9yqy83KcqzxWEdU7SpyX9WtL/SPq2pE9IWi1pcvr4gZI2pPc7JN0u6e70tlPGPc3E/7ekDuAc4AJJ90g6QdJvJI1J19svv23WKNynzMrP/cqsvNynzMrP/cqsvNyn6o8rCOuYpNcC04FjSH6XdwM/G+IpW4CpEfFnSYcB3wYmF1sxIjZIugJ4MiI+n77eauA04Pvp6343Ip4ry5sxqwHuU2bl535lVl7uU2bl535lVl7uU/XJFYT17QTgexHxVET8CVi2i/XHAF+TtB74DnDEbr7elcAH0/sfBL6xm883q3XuU2bl535lVl7uU2bl535lVl7uU3XIFYSNqY/nk7975i2/ANgMHJ0+/ufd2WhE/Cgt/Z0CtEbEvaMP1awuuE+ZlZ/7lVl5uU+ZlZ/7lVl5uU/VMFcQ1rc1wDsk7SVpX+Dt6fINwGvT++/KW/+FwKMRsQP4ANC6i+0/AexbsGwp8C2ckbfG5D5lVn7uV2bl5T5lVn7uV2bl5T5Vh5wgrGMRcTdwLfAL4CbgrvShzwMflvRz4MC8p3wZ6JH0C+BwYNsuXuIG4K/7B/5Ml10N7E8yJoBZQ3GfMis/9yuz8nKfMis/9yuz8nKfqk+KiKxjsDKRNJe8gTor9BrvAk6PiA9U6jXMaoX7lFn5uV+ZlZf7lFn5uV+ZlZf7VH3wGIQ2bJIWAqcAp2Ydi1kjcJ8yKz/3K7Pycp8yKz/3K7Pycp8qD1cQmpmZmZmZmZmZNTGPQWhmZmZmZmZmZtbEnCA0MzMzMzMzMzNrYk4QmpmZmZmZmZmZNTEnCM3MzMzMzMzMzJqYE4RmZmZmZmZmZmZN7P8HWRYoIuUtwesAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now let's explore the data with boxplots\n",
    "\n",
    "# reorganise for plotting\n",
    "df = data.melt(id_vars=['quality'])\n",
    "\n",
    "# capture variables\n",
    "cols = df.variable.unique()\n",
    "\n",
    "# plot first 6 columns\n",
    "g = sns.axisgrid.FacetGrid(df[df.variable.isin(cols[0:6])], col='variable', sharey=False)\n",
    "g.map(sns.boxplot, 'quality','value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:42.469879Z",
     "iopub.status.busy": "2021-01-23T12:18:42.469173Z",
     "iopub.status.idle": "2021-01-23T12:18:43.324646Z",
     "shell.execute_reply": "2021-01-23T12:18:43.324071Z"
    },
    "papermill": {
     "duration": 0.895901,
     "end_time": "2021-01-23T12:18:43.324782",
     "exception": false,
     "start_time": "2021-01-23T12:18:42.428881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\fenotebook\\lib\\site-packages\\seaborn\\axisgrid.py:643: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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keRgzZkyPZds6HhRrM/8F/AewoZvtNTdbluuUlZukHYCfAZ+PiFf6co5qGsB95MiRPZbNbOv4GsCKOYFhVaGpqWlj87ENGzYwceLEnCOyPFx44YU9lm3reFCsTSR9AFgREQ/091zVdLHlOmXlJGkYWfLiuoj4eRe7bBzAPSkc3L0qLVu2rMeymW2dpqYmhgzJLlnr6up8DWBOYJhZ9WhoaKCjxb4kGhoaco6oenlQrM28GzhJUitwA3C0pP8t2qfmZstqaGjY2OpizJgxrlNWMql71Y+BJyPiO93sNguYmGYjeSfwckRU9RX/sGHDeiyb2dapr6+nsbERSTQ2NlJfv1nDRxtknMCwqtDc3NzpwtV3jAenxYsXd+pb/MAD/b5ZPmh5UKzOIuKCiNgzIsYAp5LNhHV60W41OVvWhRdeyPbbb+/WF1Zq7wY+TpYMfCg9jpf0aUmfTvvMBp4CWoAfAZ/NKdaSWbVqVY9lM9t6TU1NHHTQQW59YYATGFYl5s+fz/r164HsYssD+AxOF198cafylClT8gmkBnhQrN6RdKmkk1KxJmfLamho4Ne//rVbX1hJRcQ9EaE0mPsh6TE7In4YET9M+0REnBUR+0XEQRGxOO+4+8vjypiVXn19PVdddZVbXxjgBIZVCQ/gY+A7W6XkOtW9iLgrIj6Qli+KiFlp2bNlmVmPPK6MmVl5OYFhVcED+Biw8YK7u7L1nuuUmVnpeVwZM7PycgLDqoIH8DHILrR7KlvvuU5Zh5aWFk444QRaWlryDsWsJnhcGTOz8nECw6pGtQ/gI2m6pBWSHutmuyRdJalF0iOS3l6wrUnS/6VHU1o3XNKvJf1R0uOSLi/Y/wxJKwsGTvtk+V9h+b3rXe/qsWxbp9rrlJXG1KlTWb16NVOnTs07FLOa4HFlzMzKxwkMqxo1MIDPDKCxh+3HAfunxyTgBwCSdgOmAEcAhwNTJO2ajrkiIt4KHAq8W9JxBee7sWDgtGtL+kpyUnyH2HeM+6cG6pT1U0tLC62trQC0tra6TpmZmVlFcwLDbIBExELghR52mQDMTKOyLwJ2kTQKeD8wLyJeiIgXgXlAY0SsiYgF6dyvAw8Ce5b3VeRryZIlPZZt67jrgBW3unArDDMzM6tkTmCYVY7RwLMF5SVpXXfrN5K0C3AicEfB6pNTV5SbJe3V3ZNKmiRpsaTFK1eu7OdLKC8P4lla7jpgHa0vuiubmZmZVRInMMyqnKShwPXAVQXTOt4CjImIg8labDR3d3xETIuIsRExdsSIEeUPuB/a29t7LFvvueuAARtnS+iubGZmZlZJnMAwqxxLgcKWEnumdd2t7zAN+L+I+K+OFRHRFhFrU/Fa4LByBDzQRo0a1am8xx575BRJ9XPXAQM2myXBsyaYmZlZJXMCw6xyzAImptlI3gm8HBHLgNuBYyXtmgbvPDatQ9JUYGfg84UnSmNndDgJeHIA4h9wEZF3CFXLXQcMYNddd+2xbGZmlrc777yTI488kgULFuQdilWAsiUwJO0laYGkJ9IUj5PT+oslLS2Y3vH4gmMuSFNI/knS+8sVm1keJF0P3Au8RdISSWdK+rSkT6ddZgNPAS3Aj4DPAkTEC8DXgPvT49KIeEHSnsBXgQOAB4umSz0n1buHgXOAMwbmVZbXsmXLeixb77nrgAE0NzdTV1cHQF1dHTNnzsw5IjMz6w1J0yWtkPRYF9vOkxSSds8jtlL7+te/DsBll12WcyRWCco5Al47cF5EPChpR+ABSfPStu9GxBWFO0s6ADgV+EdgD2C+pDdHxPoyxmg2YCLitC1sD+CsbrZNB6YXrVsCqJv9LwAu6FuklWuvvfbi2Wef7VS2vrnwwgv55Cc/2alsg8/8+fNZvz77ml2/fj3z5s3j3HPPzTkqMzPrhRnANUCnzHMauP1Y4JkcYiq5O++8c+OYZ+3t7SxYsICjjjoq56gsT2VLYKSm78vS8t8lPUnRzAlFJgA3pH77f5XUAhxOdsfazIw3velNnRIY++23X47RVLeGhgbGjBlDa2srY8aMoaGhIe+QLAfjxo1j9uzZtLe3M3ToUMaPH593SGZm1gsRsVDSmC42fRf4D+BXAxtReXS0vuhw2WWXDdoExtVXX93vQdeXLs2G0Rs9uqfL8i1raGjgc5/7XL/O0VcDMgZGqlyHAvelVWen6R2npz790IupItO5qmbKRzMrrfvvv79T+fe//31OkdSGs88+myFDhuT2BVRJJG0r6feSHk7dry7pYp+9U9fIP6TvsOO7Olc1aWpqYsiQ7KdAXV0dEydOzDkiMzPrK0kTgKUR8fAW9qua6ynPQFdar776Kq+++mreYfRLObuQACBpB+BnwOcj4hVJPyDrzx/p328D/9bb80XENLJZFxg7dqxH8DMbRA4//HDuuuuuTmXru4ULFxIRLFy4kMMOq4mJavpjLXB0RKySNAy4R9JtEbGoYJ8LgZsi4gep2+NsYEwOsZZMfX09jY2N3HLLLTQ2NlJfX593SGZm1geShgNfIes+0qNqup4aOnRop6TF0KFlv3ytWKW44TR58mQArrzyyn6fKy9lbYGRfgT+DLguIn4OEBHLI2J9RGwgG6iw4wpkS1NFmtkg99BDD/VYtt5ra2tjzpw5RARz5syhra0t75ByFZlVqTgsPYp/1AWwU1reGXhugMIrq5NOOonhw4dz4okn5h1K1Wtra+Occ84Z9PXJzHKxH7Av8LCkVrJrqQclvTHXqPrpK1/5SqfyV7/61ZwisUpRzllIBPwYeDIivlOwvnB6xw8BHSPnzgJOlbSNpH2B/QG3DzezjV566aUey9Z7zc3NbNiwAcgGb/TsEyCpTtJDwApgXkTcV7TLxcDpkpaQtb6oib43s2bNYs2aNdxyyy15h1L1mpubefTRR12fzGzARcSjEfGGiBgTEWPIuuO/PSL+lnNo/XL00UdvbHUxdOjQQTv+hW1SzhYY7wY+DhxdNGXqf0p6VNIjwFHAuQAR8ThwE/AEMAc4yzOQmJmVx/z58zuN6j1v3rwtHFH7UuvAQ8juWh0u6cCiXU4DZkTEnsDxwP9I2ux7tJr6FrslTun4vTSzgSTperLJDt4iaYmkM/OOqVw6uk50dH+wwa1sCYyIuCciFBEHR8Qh6TE7Ij4eEQel9Sel2Uo6jrksIvaLiLdExG3lis3MbLAbN25cpzsann1ik4h4CVgANBZtOpMs0U5E3AtsC+zexfHTImJsRIwdMWJEmaPtH7fEKR2/l2Y2kCLitIgYFRHDImLPiPhx0fYxEfF8XvGV0l/+8hck9XsGDqsNAzILiZlZKZx77rmdyuedd15OkVQ/zz7RmaQRknZJy9sB44E/Fu32DHBM2uf/I0tgVHYTiy1wS5zS8XtpZlZ6bW1t3HbbbUQEt912m1u3WflnITEz61CK+asLzZ8/n/nz52/1cXnOXV0pPPvEZkYBzZLqyJL7N0XErZIuBRZHxCzgPOBHks4lG9DzjIio6NHbt+Q973kPt99+e6ey9c24ceOYPXs27e3tbtVkZlYizc3NG5PD69atY+bMmZvd0LLBxS0wzKyqDBs2DIDRo0fnHEn1a2pq4qCDDhr0rS8AIuKRiDg0dW88MCIuTesvSskLIuKJiHh3RLwtdYucm2/U/ffaa691Kq9duzanSKpfYaumIUOGuF4NYp6Nxqx05s2bR8e9gohg7tyq/+q1fnILDDMbMJ6/urLU19dz1VVX5R2G5eiee+7pVL777rtziqT61dfXs8suu7BixQp23XVXt2oaxKZNm8YjjzzCtGnTuOCCC/IOx6yqjRw5ktbW1k5lG9zcAsPMzGyQKu4BU+U9YnLV1tbGihUrAFi+fLnvvg9SbW1tG8c/mTdvnj8HZv20fPnyHss2+DiBYWZmNkhtt912PZat9771rW91Kl9xxRU5RWJ5mjZt2sbZaDZs2MC0adNyjsisur33ve/tsWyDj7uQmJmZVan+Doy7evXqzcod3bS2hgfGhUWLFnUq33vvvTlFYnm64447Niu7G4lZ37lloBVzAsPMrAqVYkaXpUuXAv0fENUXr9Vrm2226TRw5zbbbJNjNGbVz92yzEpr4cKFncq/+c1vnBQc5JzAMDMbpF599dW8Q7B+6m/iqKWlhU9+8pMby9/73vdoaGjob1iD0nbbbdepTrk7zuA0atQolixZ0qlsZn03dOjQHss2+PgTYDaAJE0HPgCsiIgDu9gu4ErgeGANcEZEPJi2NQEXpl2nRkRzWn8YMAPYDpgNTI6IkLQbcCMwBmgFPhwRL5btxdmA8owuVgoNDQ0bW2GMGTPGyYt+OOSQQzp1Gzn00ENzjMby8vzzz/dYNrOts2rVqh7LNvh4EE+zgTUDaOxh+3HA/ukxCfgBQEpGTAGOAA4HpkjaNR3zA+DfC47rOP+XgTsiYn/gjlQ2M+tk7733ZsiQIVx44YVb3tm69fDDD3cqP/TQQ/kEYrl64xvf2GPZzLbODjvs0GPZBh8nMMwGUEQsBF7oYZcJwMzILAJ2kTQKeD8wLyJeSK0o5gGNadtOEbEoso62M4EPFpyrOS03F6w3M9to+PDhHHTQQW590U/jxo2jrq4OgLq6OsaPH59zRJYHT/loVlrr1q3rsWyDjxMYZpVlNPBsQXlJWtfT+iVdrAcYGRHL0vLfgJFdPaGkSZIWS1q8cuXK/r8CM7NBqKmpaWMCY+jQoUycODHniCwP48ePJ+sNCpI49thjc47IrLoVjyPjcWXMCQyzQSC1zuhyKPSImBYRYyNi7IgRIwY4MjOz2lBfX09jYyOSaGxspL6+Pu+QciVpuqQVkh7rZvuRkl6W9FB6XDTQMZZDU1PTxkEGhw0b5kSWWT+5VZMV8yCeZpVlKbBXQXnPtG4pcGTR+rvS+j272B9guaRREbEsdTVZUaaYzcyM7OK1tbXVF62ZGcA1ZF0bu3N3RHxgYMIZGPX19Rx33HHccsstHHfccYM+kWXW32nfhw8f3mmGp+HDh28chHxreMr32lG2FhiS9pK0QNITkh6XNDmt303SPEn/l/7dNa2XpKsktUh6RNLbyxWbWQWbBUxM9eGdwMupG8jtwLGSdk115ljg9rTtFUnvTDOYTAR+VXCuprTcVLDezMzKoL6+nquuusoXrfRqzKea1dTUxEEHHeREllkJjBw5sseyDT7lbIHRDpwXEQ9K2hF4QNI84AyymREul/RlspkRvkTn2ReOIJtZ4Ygyxmc24CRdT9aSYndJS8hmFhkGEBE/JJsG9XighWwa1U+kbS9I+hpwfzrVpRHR8cPws2yaRvW29AC4HLhJ0pnA08CHy/nazKqZpG2BhcA2ZN+NN0fElC72+zBwMVmXrIcj4qMDGadZjXmXpIeB54DzI+LxrnaSNIlsZi723nvvAQyvbzoSWWZWmmnfTz75ZNra2pgwYQLnnntuCaKyala2BEa6M7wsLf9d0pNkgwtOYFNT+GayZvBfomD2BWCRpF06mr+XK0azgRYRp21hewBndbNtOjC9i/WLgQO7WN8GHNO3SM0GnbXA0RGxStIw4B5Jt6XZgACQtD9wAfDuiHhR0hvyCtasBjwI7JPq3PHAL8luYm0mIqYB0wDGjh3b5XhOZla7Ro4cyWuvveZWTQYM0CCeksYAhwL30f3MCN3NslB8Ls+YYGZmJZWmLl6VisPSo/hC6d+B76WpjIkIjytj1kcR8UpHnYuI2cAwSbvnHJaZVaBhw4bR0NDg7nkGDEACQ9IOwM+Az0fEK4XbepoZoTueMcHMzMpBUp2kh8gGvJ0XEfcV7fJm4M2SfitpkaTGbs7jRLvZFkh6Yxq7CUmHk/0mbcs3KjMzq3RlnYUkNcP9GXBdRPw8re5uZoTuZl8wMzMru4hYDxwiaRfgF5IOjIjCKSCHkjVxP5LsO2qhpIMi4qWi87i5exXq70j5AEuXZj9bRo/erAHpVqmF0fJ7MebTKcBnJLUDrwKnphtbZmZm3SpbAiNl1X8MPBkR3ynY1DEzwuV0nhlhFnC2pBvIBu98OTz+hZmZDbCIeEnSAqARKExgLAHui4h1wF8l/ZksoXF/F6exQahwqr/BrhdjPl1DNs2qmZlZr5WzBca7gY8Dj6YmuQBfofuZEbqcfcHMzKzcJI0A1qXkxXbAeOCbRbv9EjgN+O/UV//NwFMDGqiVTSlaPEyePBmAK6+8st/nMjMzs82VbQyMiLgnIhQRB0fEIekxOyLaIuKYiNg/IsZ1TAWZBlA7KyL2i4iD0swKZmZmA2EUsEDSI2QtKuZFxK2SLpV0UtrndqBN0hPAAuCLabYfM7ONWlpaOOGEE/rdJcmgra2Nc845h7Y2/6k1s8yAzEJiZmZWySLikYg4NCXdD4yIS9P6iyJiVlqOiPhCRByQEu035Bu1mVWiqVOnsnr1aqZOnZp3KFWvubmZRx99lJkzZ+YdiplVCCcwzMzMzMxKoKWlhdbWVgBaW1vdCqMf2tramDNnDhHBnDlz3ArDzAAnMMzMzMzMSqK41YVbYfRdc3MzGzZsAGD9+vVuhWFmgBMYZmZmZmYl0dH6oruy9d78+fNpb28HoL29nXnz5uUckZlVAicwzMzMzMxKYPjw4T2WrffGjRvH0KHZhIlDhw5l/PjxOUdkZpXACQwzMzMzsxJ47bXXeixb7zU1NTFkSHapUldXx8SJE3OOyMwqgRMYZmZmZmYlEBE9lq336uvraWxsRBKNjY3U19fnHZKZVYCheQdQ69ra2rjkkkuYMmWK//CamZmZ1bAhQ4awfv36TmXru6amJlpbW936wmrC1VdfnfvMRB3PP3ny5FzjaGho4HOf+1yfjnUCo8wK568+99xz8w7HrM8q4Y8u1MYfXjMzq02HH3449957b6ey9V19fT1XXXVV3mGYlURLSwsPPfYk64fvllsMQ17PWoU98NTy3GKoW/NCv47fYgJD0kjg68AeEXGcpAOAd0XEj/v1zINAW1sbt912GxHBbbfdxsSJE90Kw6pWJfzRhdr4w2tmZrXJs5DYQJA0HfgAsCIiDkzrvgWcCLwO/AX4RES8lFuQ1qX1w3fj1bcen3cYudruj7P7dXxvWmDMAP4b+Goq/xm4EXACYwuam5s3Tv+0bt06t8IwJDUCVwJ1wLURcXnR9n2A6cAI4AXg9IhYkrZ9Ezgh7fq1iLgxrb8b2DGtfwPw+4j4oKQjgV8Bf03bfh4Rl/Ynfv/RzfT3D6+ZmdWmZcuW9Vg2K5EZwDXAzIJ184ALIqI9/Wa8APhSDrGZlVVvOubtHhE3ARsAIqIdWN/zIQYwb968jYM3RQRz587NOSLLk6Q64HvAccABwGmpRVOhK4CZEXEwcCnwjXTsCcDbgUOAI4DzJe0EEBHviYhDIuIQ4F7g5wXnu7tjW3+TF2ZmZtYzST2WzUohIhaS3egqXDc3XacBLAL2HPDAzAZAbxIYqyXVAwEg6Z3Ay2WNqkaMHDmyx7INOocDLRHxVES8DtwATCja5wDgzrS8oGD7AcDCiGiPiNXAI0Bj4YEpoXE08MvyhG9mZmY9ed/73tdj2WyA/BtwW3cbJU2StFjS4pUrVw5gWGb915suJF8AZgH7SfotWdP2U8oaVY1Yvnx5j2UbdEYDzxaUl5C1pij0MPAvZN1MPgTsmBKIDwNTJH0bGA4cBTxRdOwHgTsi4pWCde+S9DDwHHB+RDxeHJSkScAkgL333rtvr8y2WiUMiuoBUTeRtC2wENiG7Lvx5oiY0s2+JwM3A++IiMUDF6WZVbrTTz+du+66q1PZbCBJ+irQDlzX3T4RMQ2YBjB27FjP9WtVZYsJjIh4UNL7gLcAAv4UEevKHlkNOOKIIzp9iR1xRPG1qtlmzgeukXQG2cXUUmB9RMyV9A7gd8BKsq4ixV25TgOuLSg/COwTEaskHU/WMmP/4if0l1g+KmFQVA+I2sla4OhUX4YB90i6LSIWFe4kaUdgMnBff5+wEpJY4ESWWSn99Kc/3ax8wQUX5BSNDTbp9+MHgGOiox+7WY3pzSwkxRMvv10SETGzywM2HdfV6LgXA/9OdgEG8JWImJ22XQCcSXZRdk5E3L41L6QSPfroo53Kjz32WE6RWIVYCuxVUN4zrdsoIp4ja4GBpB2AkztGkI6Iy4DL0rafkA2oSyrvTtZF5UMF53qlYHm2pO9L2j0ini/ty7K+8qColTMgavqhtyoVh6VHVz/+vgZ8E/hif5+zEpJY4ESWWSndcccdm5WdwLCBkAaK/w/gfRGxJu94zMqlN11I3lGwvC1wDNmd3R4TGHQ9Oi7AdyPiisIVaSDDU4F/BPYA5kt6c0RU9WChbW1tncrPP+/rxkHufmB/SfuSJS5OBT5auENKRLwQERvIRo+entbXAbtERJukg4GDgcJRYU8Bbo2I1wrO9UZgeUSEpMPJxrzp/KE0s41SPXsAaAC+FxH3FW1/O7BXRPxaUrcJjK3pluUkVqZSEllm/dUx+1x3ZbNSkHQ9cCSwu6QlwBSy343bAPPS4LGLIuLTuQVpVia96ULSqT2npF3IBh/c0nELJY3pZRwTgBsiYi3wV0ktZHeT7+3l8WYVL01rdTZwO9k0qtMj4nFJlwKLI2IW2ZfRNyQFWReSs9Lhw4C70xfSK2TTqxb+KjoV6DQlK1lS4zOS2oFXgVPdnNCseylpfkj6nvuFpAMj4jEASUOA7wBn9OI87pZlNkilVsqdymalFhGndbH6xwMeiFkOetMCo9hqYN9+POfZqVvKYuC8iHiRbHDDwn7GS9K6zXjAQatmqcvU7KJ1FxUs30w2OGDxca+RzUTS3XmP7GLdNWStoMxsK0TES5IWkM3009H3b0fgQOCudEHyRmCWpJM8kGdlqIQxRTyeiBXfJ/B9g/5paWlh8uTJXHnllTQ0NOQdjplVgN6MgXELm/oBDyG7iLqpj8/3A7L+w5H+/TbZND+95jtbZvlYunQpdWtedlNvoG5NG0uXullwLZE0AliXkhfbAePJxroAICJeBnYv2P8uspl9nLyoEJUwpojHEzErralTp7J69WqmTp3KjBkz8g7HrF/8WzrT39/RvWmBUTheRTvwdEQs6cuTRcTGb3RJPwJuTcUtDm5oZmZWRqOA5jQOxhDgpoi4taiLl1U4jyni8USsdrS0tNDa2gpAa2srLS0tboVhZr0aA+M3pXoySaMiYlkqfohNTXNnAT+R9B2yQTz3B35fquc1s/4bPXo0f1s7dNBfHEB2gTB69Mi8w7ASiohHgEO7WH9RF7t32W3LzMxKZ+rUqZuV3QrDqpl/S2f6+zu62wSGpL/T9RRyIptxbqeeTtzN6LhHSjoknbcV+BTZyR6XdBPwBFkrj7OqfQYSMzMzMxtcttlmG9auXdupbH3T0fqiu7KZDU7dJjAiYsf+nHhrR8eNiMuAy/rznGZmZmZmeSlMXnRVtt7bYYcdWLVqVaeymVmvZyGR9AZg245yRDxTlojMzMzMzGxQW7duXY9lMxucejMLyUlks4XsAawA9gGeBP6xvKGZmZnVLo9Gvoln9jGzYqNGjerUbWTUqFH5BWNmFWNIL/b5GvBO4M8RsS9wDLCorFGZmZmZmdmgtXz58h7LZjY49aYLybqIaJM0RNKQiFgg6b/KHVgluPrqq2lpaSnpOSdPnrzVxzQ0NPC5z32upHGYmVm+PBr5Jp7Zx6z29Pd39PDhw3n11Vc7lfvyOxr8W9qslvQmgfGSpB2Au4HrJK0AVpc3LDMzMzMzG6xGjhxJW1tbp7KZWW8SGAuAnYHJwOlp+dJyBlUp+pupff/737/ZVFpXXnllf8Myy03dmhdy768/5LVXANiwbY8zOZdV3ZoXAP+QMqskHlMkUynjiUiaDnwAWBERB3axXcCVwPHAGuCMiHhwYKO0cipFi4eTTz6ZtrY2JkyYwLnnnluCqMys2vUmgTEUmAu8ANwI3BgRbT0fYgCXXXYZ559//sby17/+9RyjMeufhoaGvEMAoKXl7wA0vCnPBMLIfr8fvtjKVMrFlpmV3AzgGmBmN9uPA/ZPjyOAH6R/zTYaOXIkr732GhMnTsw7FDOrEFtMYETEJcAlkg4GPgL8RtKSiBhX9uiq3NixYzcub7PNNhx22GE5RmPWP5XSd7Sj/6tbM5lZIY8pkqmU8UQiYqGkMT3sMgGYGREBLJK0i6RREbFsYCK0ajBs2DAaGhqor6/POxQzqxC9aYHRYQXwN6ANeEN5wqk9++67L3/961/d+sLMOvHFVqZSLrbMbMCNBp4tKC9J66o6gSGJLCezqWxmZqWzxWlUJX1W0l3AHUA98O8RcXC5A6sVO+20E29729vc+sLMzMysDyRNkrRY0uKVK1fmHU6PCpMXXZXNzKx/etMCYy/g8xHxUJljMTMzM7PBYSnZb8wOe6Z1m4mIacA0gLFjxzojYDZA+jsVbql0xNDXaXRLxdPxVobejIFxwUAEYrYlbW1tXHLJJUyZMqVq+0JKaiQbdb0OuDYiLi/avg8wHRhBNnDu6RGxJG37JnBC2vVrEXFjWj8DeB/wctp2RkQ85BHezXpP0rbAQmAbsu/GmyNiStE+XwA+CbQDK4F/i4inBzpWsxoxCzhb0g1kg3e+7PEvzCpLS0sLDz32JOuH75ZrHENez/KWDzy1PLcYshnorBJszRgYZrmaNm0ajzzyCNOmTeOCC6ovryapDvgeMJ6sr+/9kmZFxBMFu11BNqhZs6SjgW8AH5d0AvB24BCyC6y7JN0WEa+k474YETcXPaVHeDfrvbXA0RGxStIw4J5UxxYV7PMHYGxErJH0GeA/yQa37jNPTZzx1MS1R9L1wJHA7pKWAFOAYQAR8UNgNlmCvYUsyf6JfCI1s56sH77boB+vCyjZd3Xe3/u18J3vBIZVhba2NubOnQvA3LlzmTRpUjW2wjgcaImIpwDSXacJQGEC4wDgC2l5AfDLgvULI6IdaJf0CNAI3NTD83mEd7NeSvVkVSoOS48o2mdBQXERcHp/ntNTExfq/9TE4B+GUDnJoIg4bQvbAzhrgMLptXI0me9Ls3c3VTerPZXwvV8L3/lOYFhVmDZt2saBsCKiWlthdDXienGLiIeBfyHr+vEhYEdJ9Wn9FEnfBoYDR9E58XGZpIvIBtv9ckSs7eb5NhvhXdIkYBLA3nvv3Z/XZ1bVUiupB4AG4HsRcV8Pu58J3NbNeXpVpyrl4qRWpib2D8MOpUkGmZlZaVXC934tfOeXLYEhaTrwAWBFRByY1u0G3AiMAVqBD0fEi+6rb1syf/78zcpVmMDojfOBaySdQdYffymwPiLmSnoH8Duyvvf3AuvTMReQTXH8D2SDnH0JuLS3T+jB0cwyEbEeOETSLsAvJB0YEY8V7yfpdGAs2dgzXZ3HdSoH/mFopdDfz9GvfvUrvvvd724sn3feeZx44on9DcvMzJJytsCYAVwDzCxY92Xgjoi4XNKXU/lLuK++bcH69et7LFeJLY64HhHPkbXAQNIOwMkR8VLadhlwWdr2E+DPaX1Hi4q1kv6bLAnSq+ezfLm5e+U0dy8UES9JWkDWTatTAkPSOOCrwPtSSyczs40mTJjQKYHh5IWZWWmVLYEREQsljSlaPYFsQCeAZuAusgSG++rbYHA/sL+kfckSCacCHy3cQdLuwAsRsYGsZcX0tL4O2CUi2iQdDBwMzE3bRkXEstSS6YNsuuDyCO8VrBKaeLu5+yaSRgDrUvJiO7LBdr9ZtM+hwP8DGiNiRQ5hmlkV2GOPPXjuuec477zz8g7FzKzmDPQYGCMLLqD+xqbbbr3qqw/ur2/VKyLaJZ0N3E42jer0iHhc0qXA4oiYRZbg+4akIOtC0jHA2TDg7ixHwStk06u2p23XpYsvAQ8Bn07rPcJ7BXNz94ozCmhOycIhwE0RcWtR/fwWsAPw01QXn4mIk3KL2Mwq0ogRIxgxYoRbX5iZlUFug3hGRKSLtK09zn2LB6GOuxmF5WoUEbPJEguF6y4qWL4ZKJ4OlYh4jWwmkq7OeXQ36ytyhHezShQRjwCHdrG+sH6OG9CgzMzMzKyTIQP8fMsljYKs2TvQ0QTXffWtR/vvv3+n8pvf/OacIjEzMzMzM7M8DHQCYxbQlJabgF8VrJ+ozDtxX30rct99nWczXLRoUU6RmJmZmZmZWR7KOY3q9WT9+XeXtASYAlwO3CTpTOBp4MNpd/fVtx5lvSG6L5uZmZlZ6Vx99dW0tLTkGkPH83eM2ZSXhoaGihi7yszKOwvJad1sOqaLfd1X33q0du3aHstmZmZmVjotLS089NiTrB++W24xDHk9u2H1wFPLc4shm+7bzCpFboN4mpmZmZlZ5Vo/fDdefevxeYeRq+3+OHvLO5nZgBnoMTDMzMzMzMzMzLaaW2DYgOhvP8rtt9+e1atXdyr3tT+k+zGamZmZWTWTNB34ALAiIg5M63YDbgTGAK3AhyPixbxiNCsHt8CwqrDPPvv0WDYzMzMzG0RmAI1F674M3BER+wN3pLJZTXELDBsQpWjxcMIJJ7B69WoOO+wwvv3tb5cgKjMzMzOz6hMRCyWNKVo9gWwWSIBm4C7gSwMXlVn5OYFhVWOfffbh6aef5itf+UreoZiZmZnVtKVLl1K35uVBP4hl3Zo2li5tzzuM3hoZEcvS8t+Akf05mT8Dm1TZ56CmuQuJVY1hw4bR0NBAfX193qGYmZmZmVWsiAggutomaZKkxZIWr1y5coAjM+sft8AwMzMzM7NORo8ezd/WDvU0qn+czejR/WrIMJCWSxoVEcskjQJWdLVTREwDpgGMHTu2yyQH+DNQqMo+BzXNCQwzMxv0JG0LLAS2IftuvDkiphTtsw0wEzgMaAM+EhGtAxyqmXWjvzOelUpHDH2dLa1UPOvaoDQLaAIuT//+Kt9wzErPCQwzMzNYCxwdEaskDQPukXRbRCwq2OdM4MWIaJB0KvBN4CN5BGtmm2tpaeGhx55k/fDdco1jyOvZDe0HnlqeWwx1a17I7bltYEi6nmzAzt0lLQGmkCUubpJ0JvA08OH8IjQrj5pOYFRCJt5ZeLNNSlEnn3zySV5//XUmTpzIrrvu2qdzuD5YsdRXeFUqDkuP4ma1E4CL0/LNwDWSlI41swqwfvhubu4OHnRxEIiI07rZdMyABmI2wGo6gVEJmXhn4c1K6/XXXwfgmWee6XMCw6wrkuqAB4AG4HsRcV/RLqOBZwEiol3Sy0A98HzReSYBkwD23nvvcodtZmZmNmjUdAIDnIkHZ+EriaRG4EqgDrg2Ii4v2r4PMB0YAbwAnB4RS9K2bwInpF2/FhE3pvXXAWOBdcDvgU9FxDpJR5L1ffxrOubnEXFp+V7dlvW31cOdd97Jww8/vLH8wQ9+kKOOOqq/YZkBEBHrgUMk7QL8QtKBEfFYH87Tq8HRzMwqXd2aF3L9HTnktVcA2LDtTrnFkN0I9OCNZpWi5hMYZpUi3d39HjAeWALcL2lWRDxRsNsVwMyIaJZ0NPAN4OOSTgDeDhxCNsjgXal//ivAdcDp6fifAJ8EfpDKd0fEB8r80gbM17/+9U7lyy67zAkMK7mIeEnSAqARKExgLAX2ApZIGgrsTDaYp5lZzWloaMg7BFpa/g5Aw5vyTCCMrIj3wswyTmCYDZzDgZaIeApA0g1kfeoLExgHAF9IywuAXxasXxgR7UC7pEfILq5uioiNt0Yk/R7Ys5wvIk/t7e09ls36StIIYF1KXmxHlmj8ZtFuHaO73wucAtzp8S/MrFZVwlhRHWPIXXnllTlHYmaVIpcEhqRW4O/AeqA9IsZK2g24ERgDtAIfjogX84jPrEw29p9PlgBHFO3zMPAvZN1MPgTsKKk+rZ8i6dvAcOAoOic+SDMnfBwoHDH2XZIeBp4Dzo+Ix4uDcn99MwBGAc2ppdQQsuTgrZIuBRZHxCzgx8D/SGoh6+J1an7hmpmZlV/e3YjAXYmsszxbYBwVEYUDn30ZuCMiLpf05VT+Uj6hmeXmfLKZDc4AFpI1WV8fEXMlvQP4HbCS7A7w+qJjv0/WSuPuVH4Q2CdNC3k8WWuO/Yuf0P31zSAiHgEO7WL9RQXLrwH/OpBx2cApxSxJf/7zn1m7di2f/exnGTZsWJ/P45mS+mbp0qXUrXk594utSlC3po2lS91K0fqnUrrOuCuRFaqkLiQTyOYyBmgG7sIJDKstHf3nO+yZ1m0UEc+RtcBA0g7AyRHxUtp2GXBZ2vYT4M8dx0maQjbw56cKzvVKwfJsSd+XtHtR4tDMzEpkw4YNbNiwgeXLl7PnnjXbm8/MBolKSaS6K5EVyiuBEcBcSQH8v3QHeGRELEvb/0Y3bXTc3N2q2P3A/pL2JUtcnAp8tHAHSbsDL0TEBuACshlJOgYA3SUi2iQdDBwMzE3bPgm8HzgmHddxrjcCyyMiJB1O1izeAw6aWSfr1q3j6aefpq2tjfr6+rzDyU1/f6i3tbVx2mmnAbBq1SouuuiiQf1+5mH06NH8be3QQT/7HGQz0I0e7ebuZlZ78kpg/HNELJX0BmCepD8WbkwXXF02ZXdzd6tWEdEu6WzgdrJpVKdHxONFfeyPBL6RPv8LgbPS4cOAuyUBvEI2vWpH29AfAk8D96btHdOlngJ8RlI78CpwqgccNKstpej28Mc//pENGzbQ1NTEfvvt16dzuMsDNDc3s2FDlkNev349M2fO5Nxzz805KjMzs9qSSwIjIpamf1dI+gXZ7AzLJY2KiGWSRgEr8ojNulaKH8n91fH8Hc3I8tKfH+ppxpDZResK+9jfDNzcxXGvkc1E0tU5u6zHEXENcE2fAq1Qo0aNYtmyZRvLe+yxR47RmFW/devWbbzoXrVqFevWrevX2A2D2fz58zfOjNTe3s68efOcwDAzMyuxAU9gSNoeGBIRf0/LxwKXsml6usvTv78a6Nisey0tLTz02JOsH75bbjEMeT1rPPDAU8tziyEbgdjy8tJLL3Uqv/iiJyqywa2/rR7OO++8TuXhw4dzxRVX9Oucg9W4ceO45ZZbiAgkMX78+LxDGpQ8Y0LGMyaY1Z5S3FAu1Q3hPFte5tECYyTwi9TUfSjwk4iYI+l+4CZJZ5I1h/9wf5/Io1FnSjUS9frhuw36fqWD/bOUt0MOOYR77713Y/nQQzebNMLMtsIDDzzQqbx48eKcIql+J510ErNmzQIgIjjxxBNzjih/khrJpgWvA66NiMuLtp8BfItNA1pfExHX9vX5KmWGAM+YYGaVarvttss7hH4b8ARGRDwFvK2L9W3AMQMdj5lVj4ceeqhT+Q9/+EM+gZiZFZk1axaSNrbAuOWWWwZ1F5I0+PT3gPHAEuB+SbMi4omiXW+MiLNL8ZyVMg6LZ0wws3KolL9xeaukaVRLzqNRZzwStdWKV199tceymVle5s+fT8c4yRHhMTCy8c1a0o0rJN0ATACKExhmZma9NiTvAMzMzMyq3bhx4xg6NLsvNHToUI+BAaOBZwvKS9K6YidLekTSzZL26upEkiZJWixp8cqVK8sRq5mZVQknMMysahx22GGdymPHjs0pErPaMGTIkB7L1ntNTU0b37+6ujomTpyYc0RV4RZgTEQcDMwDmrvaKSKmRcTYiBg7YsSIAQ3QzMwqS013ITGz2lJfX99j2cy2zrhx45g7d+7GslsN9F19fT2NjY3ccsstNDY2+u9TNjBnYYuKPdk0WCewcfyzDtcC/zkAcdkA8YwJZlYOTmBYr3hGl0ypZnSxvrnnnns6le+++24uuOCCnKIxq36f+tSnmDdv3saBJydNmpR3SFWtqamJ1tZWt77I3A/sL2lfssTFqcBHC3eQNCoilqXiScCTAxuiVbpamDHBzErLCQwzqxr//M//3Olu8Xve854co6l+a9as4S9/+QstLS2Dfrq91Pd+JtlU3wFMi4gri/bZGfhfYG+y788rIuK/BzrWUqqvr2f8+PHMnTuXY4891q0G+qm+vp6rrroq7zAqQkS0SzobuJ1sGtXpEfG4pEuBxRExCzhH0klAO/ACcEZuAVvJucWDmZVDzScw6ta8kGurgSGvvQLAhm13yi2GujUvkP0m7zvP6JLxjC75kpR3CDXlmWeeYcOGDUydOpUZM2bkHU7e2oHzIuJBSTsCD0iaVzTl41nAExFxoqQRwJ8kXRcRr+cScYl86lOf4m9/+5tbX1jJRcRsYHbRuosKli8A3IzOutXS0sLkyZO58sorB32i3cwyNZ3AqIQ/dC0tfweg4U15XvSOrIj3wqy/7r777s3Kg7ULSX/7Fq9Zs4a1a9cC0NrayqRJk/rcVLcW+hanZuzL0vLfJT1JNmNCYQIjgB2VZdJ2ILtjXPV9ytxqwMwq1dSpU1m9erUT7Wa2UU0nMCrhB3XHoENXXnnlFvY0sy0ZN24cs2fPpr293dMU9tMzzzzTqfz000/z1re+NadoKoukMcChwH1Fm64BZgHPATsCH4mIDQMbnZnZ4NDS0kJrayuQJdrd3dHMoMYTGFZa7o5Tmu441ndNTU3MmTMH8DSF/U3QHnnkkZ3Ka9eudaIVkLQD8DPg8xHxStHm9wMPAUcD+wHzJN1dvJ+kScAkgL333rvsMZuZ1aKpU6duVnYrDDNzAsN6pRIy3u6OY/X19Rx11FHcfvvtHHnkkR5wsB/GjBmz8c5WR3mwkzSMLHlxXUT8vItdPgFcHhEBtEj6K/BW4PeFO0XENGAawNixY6O8UZuZ1abC76iuymY2ODmBYb3i7jhWKV555ZVO/1rfXHjhhXzyk5/sVB7M0rgWPwaejIjvdLPbM8AxwN2SRgJvAZ4aoBDLpq2tjUsuuYQpU6Y4KWhmFcOJdjPrypC8AzAz6622tjYWLVoEwKJFi2hra8s5ourV0NCw8cfgmDFj3LII3g18HDha0kPpcbykT0v6dNrna8A/SXoUuAP4UkQ8n1fApdLc3Myjjz7KzJkz8w7FzGyj4sT6YE+0m1nGCQyzASSpUdKfJLVI+nIX2/eRdIekRyTdJWnPgm3flPRYenykYP2+ku5L57xR0j+k9dukckvaPmZAXmQZXX311WSt9yEiuPrqq3OOqLpdeOGFbL/99v5RCETEPRGhiDg4Ig5Jj9kR8cOI+GHa57mIODYiDoqIAyPif/OOu7/a2tqYM2cOEcGcOXOcFDSziuFEu5l1xV1IetDfaQqBjcd3dH/oi1qYorBS3kvI7/2UVAd8DxgPLAHulzQrIgqnabwCmBkRzZKOBr4BfFzSCcDbgUOAbYC7JN2WBg/8JvDdiLhB0g+BM4EfpH9fjIgGSaem/T5CFfvNb37TY9m2TkNDA7/+9a/zDsNy1NzczIYN2UQq69evZ+bMmZx77rk5R2WWn0r5vVILv/1K4cILL2Ty5MlOtFcx1ykrtYprgbGlO9TVZrvttmO77bbLO4yaUAPv5eFAS0Q8FRGvAzcAE4r2OQC4My0vKNh+ALAwItojYjXwCNCY+u0fDdyc9msGPpiWJ6Qyafsxaf+q1dH6oruymW2d+fPn097eDkB7ezvz5s3LOSKz6lcDv1cqRkei3a0vBjfXKStUUS0wenmHesA4S1c6fi8BGA08W1BeAhxRtM/DwL8AVwIfAnaUVJ/WT5H0bWA4cBTwBFAPvBQR7QXnHF38fBHRLunltH+nPvvVNOXjnnvuyZIlSzqVzazvxo0bx+zZs2lvb2fo0KGMHz8+75DMcuXfK2al5TplpVZpLTB6c4farJadD7xP0h+A9wFLgfURMReYDfwOuB64F1hfiieMiGkRMTYixo4YMaIUpyybiy++uMeymW2dpqYmhgzJfgrU1dUxceLEnCMyMzMz616lJTC6ukM9unAHSZMkLZa0eOXKlQManFk/LQX2KijvmdZtlAYJ/JeIOBT4alr3Uvr3sjSw4HhAwJ+BNmAXSUO7OOfG50vbd077V62GhoaNrS723HNPNyk166f6+noaGxuRRGNjo6dRNTMzs4pWaQmMLaqmu8VmRe4H9k+zhvwDcCowq3AHSbtL6qiXFwDT0/q61JUESQcDBwNzIxsEYgFwSjqmCfhVWp6VyqTtd0YNDBpx8cUXs/3227v1hVmJNDU1cdBBB7n1hZmZmVW8ihoDg17coTarVmkcirOB24E6YHpEPC7pUmBxRMwCjgS+ISmAhcBZ6fBhwN1pDM5XgNMLxr34EnCDpKnAH4Afp/U/Bv5HUgvwAlnCpOp55gyz0qqvr+eqq67KOwwzMzOzLaq0BMbGO9RkiYtTgY/mG5JZ6UTEbLKxLArXXVSwfDObZhQp3Oc1splIujrnU2Tjx3R1zL/2M2QzMzMzM7OKoEprUS7peOC/2HSH+rIe9l0JPD1AofXH7hTN/GB9Vi3v5fMR0Zh3EFvLdWpQqpb30nWqvKrlc1ANquW9dJ0qr2r5HFSDankvXafKq1o+B9WgWt7LLutUxSUwapGkxRExNu84aoHfSwN/DkrJ76WBPwel5PfSwJ+DUvJ7aeDPQSlV+3tZdYN4mpmZmZmZmdng4wSGmZmZmZmZmVU8JzAGxrS8A6ghfi8N/DkoJb+XBv4clJLfSwN/DkrJ76WBPwelVNXvpcfAMDMzMzMzM7OK5xYYZmZmZmZmZlbxnMAwMzMzMzMzs4rnBEYZSWqU9CdJLZK+nHc81UzSdEkrJD2WdyyWH9ep0nGdMnCdKiXXKQPXqVJynTJwnSqlWqlTTmCUiaQ64HvAccABwGmSDsg3qqo2A2jMOwjLj+tUyc3AdWpQc50quRm4Tg1qrlMlNwPXqUHNdarkZlADdcoJjPI5HGiJiKci4nXgBmBCzjFVrYhYCLyQdxyWK9epEnKdMlynSsp1ynCdKinXKcN1qqRqpU45gVE+o4FnC8pL0joz6xvXKbPScp0yKy3XKbPScp2yzTiBYWZmZmZmZmYVzwmM8lkK7FVQ3jOtM7O+cZ0yKy3XKbPScp0yKy3XKduMExjlcz+wv6R9Jf0DcCowK+eYzKqZ65RZablOmZWW65RZablO2WacwCiTiGgHzgZuB54EboqIx/ONqnpJuh64F3iLpCWSzsw7JhtYrlOl5TplrlOl5TplrlOl5TplrlOlVSt1ShGRdwxmZmZmZmZmZj1yCwwzMzMzMzMzq3hOYJiZmZmZmZlZxXMCw8zMzMzMzMwqnhMYZmZmZmZmZlbxnMAwMzMzMzMzs4rnBIZ1ImmMpMfS8lhJV6XlIyX9U77RmVUf1ymz0nKdMist1ymz0nKdKq+heQdglSsiFgOLU/FIYBXwu9wCMqtyrlNmpeU6ZVZarlNmpeU6VXpugVFDJH1V0p8l3SPpeknnS7pL0ti0fXdJrWl5jKS7JT2YHptlA1OW8FZJY4BPA+dKekjSeyT9VdKwtN9OhWWzWuE6ZVZarlNmpeU6ZVZarlOVzy0waoSkw4BTgUPI/l8fBB7o4ZAVwPiIeE3S/sD1wNiudoyIVkk/BFZFxBXp+e4CTgB+mZ735xGxriQvxqwCuE6ZlZbrlFlpuU6ZlZbrVHVwC4za8R7gFxGxJiJeAWZtYf9hwI8kPQr8FDhgK5/vWuATafkTwH9v5fFmlc51yqy0XKfMSst1yqy0XKeqgFtg1L52NiWqti1Yfy6wHHhb2v7a1pw0In6bmk0dCdRFxGP9D9WsKrhOmZWW65RZablOmZWW61QFcQuM2rEQ+KCk7STtCJyY1rcCh6XlUwr23xlYFhEbgI8DdVs4/9+BHYvWzQR+grOFVptcp8xKy3XKrLRcp8xKy3WqCjiBUSMi4kHgRuBh4Dbg/rTpCuAzkv4A7F5wyPeBJkkPA28FVm/hKW4BPtQx6Exadx2wK1l/L7Oa4jplVlquU2al5TplVlquU9VBEZF3DFYGki6mYJCYMj3HKcCEiPh4uZ7DrFK4TpmVluuUWWm5TpmVlutUZfIYGNYnkq4GjgOOzzsWs1rgOmVWWq5TZqXlOmVWWq5TfeMWGGZmZmZmZmZW8TwGhpmZmZmZmZlVPCcwzMzMzMzMzKziOYFhZmZmZmZmZhXPCQwzMzMzMzMzq3hOYJiZmZmZmZlZxfv/ASAuT18GkAQvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot remaining columns\n",
    "g = sns.axisgrid.FacetGrid(df[df.variable.isin(cols[6:])], col='variable', sharey=False)\n",
    "g.map(sns.boxplot, 'quality','value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:43.414024Z",
     "iopub.status.busy": "2021-01-23T12:18:43.413098Z",
     "iopub.status.idle": "2021-01-23T12:18:43.418000Z",
     "shell.execute_reply": "2021-01-23T12:18:43.418589Z"
    },
    "papermill": {
     "duration": 0.05929,
     "end_time": "2021-01-23T12:18:43.418746",
     "exception": false,
     "start_time": "2021-01-23T12:18:43.359456",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        0  \n",
       "1      9.8        0  \n",
       "2      9.8        0  \n",
       "3      9.8        0  \n",
       "4      9.4        0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:43.502924Z",
     "iopub.status.busy": "2021-01-23T12:18:43.497433Z",
     "iopub.status.idle": "2021-01-23T12:18:43.818177Z",
     "shell.execute_reply": "2021-01-23T12:18:43.818713Z"
    },
    "papermill": {
     "duration": 0.363926,
     "end_time": "2021-01-23T12:18:43.818877",
     "exception": false,
     "start_time": "2021-01-23T12:18:43.454951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the citric acid affects the pH of the wine\n",
    "\n",
    "plt.scatter(data['citric acid'], data['pH'], c=data['quality'])\n",
    "plt.xlabel('Citric acid')\n",
    "plt.ylabel('pH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:43.897422Z",
     "iopub.status.busy": "2021-01-23T12:18:43.896626Z",
     "iopub.status.idle": "2021-01-23T12:18:44.094315Z",
     "shell.execute_reply": "2021-01-23T12:18:44.094821Z"
    },
    "papermill": {
     "duration": 0.238164,
     "end_time": "2021-01-23T12:18:44.094975",
     "exception": false,
     "start_time": "2021-01-23T12:18:43.856811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the sulphates may affect the pH of the wine\n",
    "\n",
    "plt.scatter(data['sulphates'], data['pH'], c=data['quality'])\n",
    "plt.xlabel('sulphates')\n",
    "plt.ylabel('pH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:44.181205Z",
     "iopub.status.busy": "2021-01-23T12:18:44.180460Z",
     "iopub.status.idle": "2021-01-23T12:18:44.374868Z",
     "shell.execute_reply": "2021-01-23T12:18:44.375362Z"
    },
    "papermill": {
     "duration": 0.241747,
     "end_time": "2021-01-23T12:18:44.375518",
     "exception": false,
     "start_time": "2021-01-23T12:18:44.133771",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['sulphates'], data['citric acid'], c=data['quality'])\n",
    "plt.xlabel('sulphates')\n",
    "plt.ylabel('citric acid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.039476,
     "end_time": "2021-01-23T12:18:44.454965",
     "exception": false,
     "start_time": "2021-01-23T12:18:44.415489",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Good quality wine tend to have more citric acid and more sulphate, thus similar pH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:44.538786Z",
     "iopub.status.busy": "2021-01-23T12:18:44.538024Z",
     "iopub.status.idle": "2021-01-23T12:18:45.932422Z",
     "shell.execute_reply": "2021-01-23T12:18:45.931871Z"
    },
    "papermill": {
     "duration": 1.437649,
     "end_time": "2021-01-23T12:18:45.932539",
     "exception": false,
     "start_time": "2021-01-23T12:18:44.494890",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's evaluate the relationship between some molecules and the density of the wine\n",
    "\n",
    "g = sns.PairGrid(data, y_vars=[\"density\"], x_vars=['chlorides','sulphates', 'residual sugar', 'alcohol'])\n",
    "g.map(sns.regplot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.042804,
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   },
   "source": [
    "## Create additional variables\n",
    "\n",
    "Let's combine variables into new ones to capture additional information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>total_acidity</th>\n",
       "      <th>average_acidity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
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       "      <td>0.076</td>\n",
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       "      <td>7.8</td>\n",
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       "      <td>0.098</td>\n",
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       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.68</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>11.48</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  total_acidity  average_acidity  \n",
       "0      9.4        0           8.10             4.05  \n",
       "1      9.8        0           8.68             4.34  \n",
       "2      9.8        0           8.56             4.28  \n",
       "3      9.8        0          11.48             5.74  \n",
       "4      9.4        0           8.10             4.05  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine fixed and volatile acidity to create total acidity\n",
    "# and mean acidity\n",
    "\n",
    "combinator = MathematicalCombination(\n",
    "    variables_to_combine=['fixed acidity', 'volatile acidity'],\n",
    "    math_operations = ['sum', 'mean'],\n",
    "    new_variables_names = ['total_acidity', 'average_acidity']\n",
    ")\n",
    "\n",
    "data = combinator.fit_transform(data)\n",
    "\n",
    "# note the new variables at the end of the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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       "      <th>fixed acidity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
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       "      <td>4.34</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.3890</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.3710</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>11.48</td>\n",
       "      <td>5.74</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.3275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
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      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  total_acidity  average_acidity  total_minerals  \\\n",
       "0      9.4        0           8.10             4.05           0.636   \n",
       "1      9.8        0           8.68             4.34           0.778   \n",
       "2      9.8        0           8.56             4.28           0.742   \n",
       "3      9.8        0          11.48             5.74           0.655   \n",
       "4      9.4        0           8.10             4.05           0.636   \n",
       "\n",
       "   average_minerals  \n",
       "0            0.3180  \n",
       "1            0.3890  \n",
       "2            0.3710  \n",
       "3            0.3275  \n",
       "4            0.3180  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's combine salts into total minerals and average minerals\n",
    "\n",
    "combinator = MathematicalCombination(\n",
    "    variables_to_combine=['chlorides', 'sulphates'],\n",
    "    math_operations = ['sum', 'mean'],\n",
    "    new_variables_names = ['total_minerals', 'average_minerals']\n",
    ")\n",
    "\n",
    "data = combinator.fit_transform(data)\n",
    "\n",
    "# note the new variable at the end of the dataframe\n",
    "data.head()"
   ]
  },
  {
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       "      <th>fixed acidity</th>\n",
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       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
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       "      <th>1</th>\n",
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       "      <td>4.34</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.3890</td>\n",
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       "      <td>7.8</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
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       "      <td>0.655</td>\n",
       "      <td>0.3275</td>\n",
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       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  total_acidity  average_acidity  total_minerals  \\\n",
       "0      9.4        0           8.10             4.05           0.636   \n",
       "1      9.8        0           8.68             4.34           0.778   \n",
       "2      9.8        0           8.56             4.28           0.742   \n",
       "3      9.8        0          11.48             5.74           0.655   \n",
       "4      9.4        0           8.10             4.05           0.636   \n",
       "\n",
       "   average_minerals  non_free_sulfur_dioxide  \n",
       "0            0.3180                     23.0  \n",
       "1            0.3890                     42.0  \n",
       "2            0.3710                     39.0  \n",
       "3            0.3275                     43.0  \n",
       "4            0.3180                     23.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's determine the sulfur that is not free\n",
    "\n",
    "combinator = CombineWithReferenceFeature(\n",
    "    variables_to_combine=['total sulfur dioxide'],\n",
    "    reference_variables=['free sulfur dioxide'],\n",
    "    operations=['sub'],\n",
    "    new_variables_names=['non_free_sulfur_dioxide']\n",
    ")\n",
    "\n",
    "data = combinator.fit_transform(data)\n",
    "\n",
    "# note the new variable at the end of the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:46.500945Z",
     "iopub.status.busy": "2021-01-23T12:18:46.500162Z",
     "iopub.status.idle": "2021-01-23T12:18:46.533085Z",
     "shell.execute_reply": "2021-01-23T12:18:46.533646Z"
    },
    "papermill": {
     "duration": 0.080649,
     "end_time": "2021-01-23T12:18:46.533797",
     "exception": false,
     "start_time": "2021-01-23T12:18:46.453148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>total_acidity</th>\n",
       "      <th>average_acidity</th>\n",
       "      <th>total_minerals</th>\n",
       "      <th>average_minerals</th>\n",
       "      <th>non_free_sulfur_dioxide</th>\n",
       "      <th>percentage_free_sulfur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.68</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.3890</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.373134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>11.48</td>\n",
       "      <td>5.74</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.3275</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  total_acidity  average_acidity  total_minerals  \\\n",
       "0      9.4        0           8.10             4.05           0.636   \n",
       "1      9.8        0           8.68             4.34           0.778   \n",
       "2      9.8        0           8.56             4.28           0.742   \n",
       "3      9.8        0          11.48             5.74           0.655   \n",
       "4      9.4        0           8.10             4.05           0.636   \n",
       "\n",
       "   average_minerals  non_free_sulfur_dioxide  percentage_free_sulfur  \n",
       "0            0.3180                     23.0                0.323529  \n",
       "1            0.3890                     42.0                0.373134  \n",
       "2            0.3710                     39.0                0.277778  \n",
       "3            0.3275                     43.0                0.283333  \n",
       "4            0.3180                     23.0                0.323529  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's calculate the % of free sulfur\n",
    "\n",
    "combinator = CombineWithReferenceFeature(\n",
    "    variables_to_combine=['free sulfur dioxide'],\n",
    "    reference_variables=['total sulfur dioxide'],\n",
    "    operations=['div'],\n",
    "    new_variables_names=['percentage_free_sulfur']\n",
    ")\n",
    "\n",
    "data = combinator.fit_transform(data)\n",
    "\n",
    "# note the new variable at the end of the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:46.629772Z",
     "iopub.status.busy": "2021-01-23T12:18:46.628994Z",
     "iopub.status.idle": "2021-01-23T12:18:46.662853Z",
     "shell.execute_reply": "2021-01-23T12:18:46.663344Z"
    },
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     "exception": false,
     "start_time": "2021-01-23T12:18:46.579895",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>total_acidity</th>\n",
       "      <th>average_acidity</th>\n",
       "      <th>total_minerals</th>\n",
       "      <th>average_minerals</th>\n",
       "      <th>non_free_sulfur_dioxide</th>\n",
       "      <th>percentage_free_sulfur</th>\n",
       "      <th>percentage_salt_sulfur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.050909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.68</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.3890</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.373134</td>\n",
       "      <td>0.027200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.043333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>11.48</td>\n",
       "      <td>5.74</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.3275</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.034118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "      <td>8.10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.050909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  total_acidity  average_acidity  total_minerals  \\\n",
       "0      9.4        0           8.10             4.05           0.636   \n",
       "1      9.8        0           8.68             4.34           0.778   \n",
       "2      9.8        0           8.56             4.28           0.742   \n",
       "3      9.8        0          11.48             5.74           0.655   \n",
       "4      9.4        0           8.10             4.05           0.636   \n",
       "\n",
       "   average_minerals  non_free_sulfur_dioxide  percentage_free_sulfur  \\\n",
       "0            0.3180                     23.0                0.323529   \n",
       "1            0.3890                     42.0                0.373134   \n",
       "2            0.3710                     39.0                0.277778   \n",
       "3            0.3275                     43.0                0.283333   \n",
       "4            0.3180                     23.0                0.323529   \n",
       "\n",
       "   percentage_salt_sulfur  \n",
       "0                0.050909  \n",
       "1                0.027200  \n",
       "2                0.043333  \n",
       "3                0.034118  \n",
       "4                0.050909  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's determine from all free sulfur how much is as salt\n",
    "\n",
    "combinator = CombineWithReferenceFeature(\n",
    "    variables_to_combine=['sulphates'],\n",
    "    reference_variables=['free sulfur dioxide'],\n",
    "    operations=['div'],\n",
    "    new_variables_names=['percentage_salt_sulfur']\n",
    ")\n",
    "\n",
    "data = combinator.fit_transform(data)\n",
    "\n",
    "# note the new variable at the end of the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:46.759146Z",
     "iopub.status.busy": "2021-01-23T12:18:46.758408Z",
     "iopub.status.idle": "2021-01-23T12:18:47.891197Z",
     "shell.execute_reply": "2021-01-23T12:18:47.890613Z"
    },
    "papermill": {
     "duration": 1.181643,
     "end_time": "2021-01-23T12:18:47.891320",
     "exception": false,
     "start_time": "2021-01-23T12:18:46.709677",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sole\\Documents\\Repositories\\envs\\fenotebook\\lib\\site-packages\\seaborn\\axisgrid.py:643: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x216 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now let's explore the new variables with boxplots\n",
    "\n",
    "new_vars = [\n",
    "    'total_acidity',\n",
    "    'average_acidity',\n",
    "    'total_minerals',\n",
    "    'average_minerals',\n",
    "    'non_free_sulfur_dioxide',\n",
    "    'percentage_free_sulfur',\n",
    "    'percentage_salt_sulfur']\n",
    "\n",
    "# reorganise for plotting\n",
    "df = data[new_vars+['quality']].melt(id_vars=['quality'])\n",
    "\n",
    "# capture variables\n",
    "cols = df.variable.unique()\n",
    "\n",
    "# plot first 6 columns\n",
    "g = sns.axisgrid.FacetGrid(df[df.variable.isin(cols)], col='variable', sharey=False)\n",
    "g.map(sns.boxplot, 'quality','value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.046682,
     "end_time": "2021-01-23T12:18:47.984972",
     "exception": false,
     "start_time": "2021-01-23T12:18:47.938290",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Machine Learning Pipeline\n",
    "\n",
    "Now we are going to carry out all variable creation within a Scikit-learn Pipeline and add a classifier at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:48.090025Z",
     "iopub.status.busy": "2021-01-23T12:18:48.089267Z",
     "iopub.status.idle": "2021-01-23T12:18:48.104744Z",
     "shell.execute_reply": "2021-01-23T12:18:48.104037Z"
    },
    "papermill": {
     "duration": 0.073052,
     "end_time": "2021-01-23T12:18:48.104874",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.031822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1279, 11), (320, 11))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('winequality-red.csv', sep=';')\n",
    "\n",
    "# make binary target\n",
    "data['quality'] = np.where(data['quality'] <= 6, 0, 1)\n",
    "\n",
    "# separate dataset into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(labels=['quality'], axis=1),\n",
    "    data['quality'],\n",
    "    test_size=0.2,\n",
    "    random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:48.241663Z",
     "iopub.status.busy": "2021-01-23T12:18:48.240390Z",
     "iopub.status.idle": "2021-01-23T12:18:48.386240Z",
     "shell.execute_reply": "2021-01-23T12:18:48.385467Z"
    },
    "papermill": {
     "duration": 0.23237,
     "end_time": "2021-01-23T12:18:48.386367",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.153997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('acidity',\n",
       "                 MathematicalCombination(math_operations=['sum', 'mean'],\n",
       "                                         new_variables_names=['total_acidity',\n",
       "                                                              'average_acidity'],\n",
       "                                         variables_to_combine=['fixed acidity',\n",
       "                                                               'volatile '\n",
       "                                                               'acidity'])),\n",
       "                ('total_minerals',\n",
       "                 MathematicalCombination(math_operations=['sum', 'mean'],\n",
       "                                         new_variables_names=['total_minerals',\n",
       "                                                              'average_minearals'],\n",
       "                                         variables_to_combine...\n",
       "                                             reference_variables=['total '\n",
       "                                                                  'sulfur '\n",
       "                                                                  'dioxide'],\n",
       "                                             variables_to_combine=['free '\n",
       "                                                                   'sulfur '\n",
       "                                                                   'dioxide'])),\n",
       "                ('perc_salt_sulfur',\n",
       "                 CombineWithReferenceFeature(new_variables_names=['percentage_salt_sulfur'],\n",
       "                                             operations=['div'],\n",
       "                                             reference_variables=['free sulfur '\n",
       "                                                                  'dioxide'],\n",
       "                                             variables_to_combine=['sulphates'])),\n",
       "                ('gbm',\n",
       "                 GradientBoostingClassifier(max_depth=2, n_estimators=10,\n",
       "                                            random_state=1))])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([\n",
    "    # variable creation\n",
    "    ('acidity', MathematicalCombination(\n",
    "        variables_to_combine=['fixed acidity', 'volatile acidity'],\n",
    "        math_operations = ['sum', 'mean'],\n",
    "        new_variables_names = ['total_acidity', 'average_acidity']\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    ('total_minerals', MathematicalCombination(\n",
    "        variables_to_combine=['chlorides', 'sulphates'],\n",
    "        math_operations = ['sum', 'mean'],\n",
    "        new_variables_names = ['total_minerals', 'average_minearals'],\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    ('non_free_sulfur', CombineWithReferenceFeature(\n",
    "        variables_to_combine=['total sulfur dioxide'],\n",
    "        reference_variables=['free sulfur dioxide'],\n",
    "        operations=['sub'],\n",
    "        new_variables_names=['non_free_sulfur_dioxide'],\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    ('perc_free_sulfur', CombineWithReferenceFeature(\n",
    "        variables_to_combine=['free sulfur dioxide'],\n",
    "        reference_variables=['total sulfur dioxide'],\n",
    "        operations=['div'],\n",
    "        new_variables_names=['percentage_free_sulfur'],\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    ('perc_salt_sulfur', CombineWithReferenceFeature(\n",
    "        variables_to_combine=['sulphates'],\n",
    "        reference_variables=['free sulfur dioxide'],\n",
    "        operations=['div'],\n",
    "        new_variables_names=['percentage_salt_sulfur'],\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    # =====  the machine learning model ====\n",
    "    \n",
    "    ('gbm', GradientBoostingClassifier(n_estimators=10, max_depth=2, random_state=1)),\n",
    "])\n",
    "\n",
    "# create new variables, and then train gradient boosting machine\n",
    "# uses only the training dataset\n",
    "\n",
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:48.509902Z",
     "iopub.status.busy": "2021-01-23T12:18:48.499863Z",
     "iopub.status.idle": "2021-01-23T12:18:48.599372Z",
     "shell.execute_reply": "2021-01-23T12:18:48.600415Z"
    },
    "papermill": {
     "duration": 0.165519,
     "end_time": "2021-01-23T12:18:48.600655",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.435136",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train roc-auc: 0.8884522340404694\n",
      "Test roc-auc: 0.8693103448275863\n"
     ]
    }
   ],
   "source": [
    "# make predictions and determine model performance\n",
    "\n",
    "# the pipeline takes in the raw data, creates all the new features and then\n",
    "# makes the prediction with the model trained on the final subset of variables\n",
    "\n",
    "# obtain predictions and determine model performance\n",
    "\n",
    "pred = pipe.predict_proba(X_train)\n",
    "print('Train roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "\n",
    "pred = pipe.predict_proba(X_test)\n",
    "print('Test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.04977,
     "end_time": "2021-01-23T12:18:48.710141",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.660371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:48.831178Z",
     "iopub.status.busy": "2021-01-23T12:18:48.830068Z",
     "iopub.status.idle": "2021-01-23T12:18:48.833302Z",
     "shell.execute_reply": "2021-01-23T12:18:48.832359Z"
    },
    "papermill": {
     "duration": 0.067956,
     "end_time": "2021-01-23T12:18:48.833462",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.765506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_vars = ['total_acidity', 'average_acidity', 'total_minerals', 'average_minearals',\n",
    "           'non_free_sulfur_dioxide', 'percentage_free_sulfur','percentage_salt_sulfur']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-23T12:18:48.954651Z",
     "iopub.status.busy": "2021-01-23T12:18:48.953527Z",
     "iopub.status.idle": "2021-01-23T12:18:49.205420Z",
     "shell.execute_reply": "2021-01-23T12:18:49.206089Z"
    },
    "papermill": {
     "duration": 0.309114,
     "end_time": "2021-01-23T12:18:49.206277",
     "exception": false,
     "start_time": "2021-01-23T12:18:48.897163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WTQMXSZIkSdIqTDIH773AscCN2+3/AJ49wfNuAvz32PZZ7b4NeSJw9AQ/V5IkSZK0iEkKvOtV1UeASwGqai1wyZUZIslBwJ7A6zdw/8FJTkxy4q9+9asr81dLkiRJ0tyYpMC7IMmfAQWQZG/g3Amedzaw9dj2Tdt960lyT+BQ4ICqumixH1RVb6+qPatqz6222mqCXy1JkiRJG59Jumg+l6Z75o5Jvg5sBRw4wfNOAHZKsj1NYfcI4FHjD0hyG+BtwH2q6pcrCS5JkiRJWt8GC7wk21TVf1XVyUnuCuwMBPhxVf1pQ88bqaq1SZ5BM39vDfDuqjotySuAE6vqSJohmVsAH00C8F9VdcAV/8+SJEmSpI3PUlfwPgXs0d7+cFU9ZKU/vKqOAo5asO8lY7fvudKfKUmSJEla3FJz8DJ2e+L17yRJkiRJ3ViqwKsN3JYkSZIk9dBSQzR3S3IezZW8q7e3aberqracejpJkiRJ0sQ2WOBV1ZpZBpEkSZIkXTGTrIMnSZIkSRoACzxJkiRJmhMWeJIkSZI0JyzwJEmSJGlOWOBJkiRJ0pywwJMkSZKkOWGBJ0mSJElzwgJPkiRJkuaEBZ4kSZIkzQkLPEmSJEmaExZ4kiRJkjQnLPAkSZIkaU5Y4EmSJEnSnLDAkyRJkqQ5YYEnSZIkSXPCAk+SJEmS5oQFniRJkiTNCQs8SZIkSZoTFniSJEmSNCcs8CRJkiRpTljgSZIkSdKcsMCTJEmSpDlhgSdJkiRJc8ICT5IkSZLmhAWeJEmSJM0JCzxJkiRJmhMWeJIkSZI0JyzwJEmSJGlOWOBJkiRJ0pywwJMkSZKkOWGBJ0mSJElzwgJPkiRJkubEVboOoPmz3Qs+O7Wf/bPX7D+1ny1JkiQNnVfwJEmSJGlOWOBJkiRJ0pyYaoGX5D5JfpzkjCQvWOT+uyQ5OcnaJAdOM4skSZIkzbupFXhJ1gBvBvYDdgEemWSXBQ/7L+BxwAenlUOSJEmSNhbTbLKyF3BGVZ0JkORDwAOAH44eUFU/a++7dIo5JEmSJGmjMM0hmjcB/nts+6x234olOTjJiUlO/NWvfnWlhJMkSZKkeTOIJitV9faq2rOq9txqq626jiNJkiRJvTTNAu9sYOux7Zu2+yRJkiRJUzDNAu8EYKck2yfZFHgEcOQUf58kSZIkbdSmVuBV1VrgGcCxwOnAR6rqtCSvSHIAQJLbJTkLeCjwtiSnTSuPJEmSJM27aXbRpKqOAo5asO8lY7dPoBm6KUmSJEm6ggbRZEWSJEmStLypXsGThmS7F3x2aj/7Z6/Zf2o/W5IkSRrxCp4kSZIkzQkLPEmSJEmaExZ4kiRJkjQnLPAkSZIkaU5Y4EmSJEnSnLDAkyRJkqQ5YYEnSZIkSXPCAk+SJEmS5oQFniRJkiTNCQs8SZIkSZoTFniSJEmSNCcs8CRJkiRpTljgSZIkSdKcsMCTJEmSpDlhgSdJkiRJc8ICT5IkSZLmhAWeJEmSJM0JCzxJkiRJmhMWeJIkSZI0JyzwJEmSJGlOWOBJkiRJ0pywwJMkSZKkOWGBJ0mSJElzwgJPkiRJkuaEBZ4kSZIkzQkLPEmSJEmaExZ4kiRJkjQnLPAkSZIkaU5Y4EmSJEnSnLhK1wEkXXHbveCzU/vZP3vN/lP72ZIkSbpyeQVPkiRJkuaEBZ4kSZIkzQkLPEmSJEmaExZ4kiRJkjQnLPAkSZIkaU5Y4EmSJEnSnJjqMglJ7gO8EVgDvLOqXrPg/qsB7wNuC/wGeHhV/WyamST1x5CXdxhydkmSNL+mVuAlWQO8GdgXOAs4IcmRVfXDsYc9EfhdVd0sySOA1wIPn1YmSdrYDbkwHXJ2SZJmZZpDNPcCzqiqM6vqYuBDwAMWPOYBwBHt7Y8B90iSKWaSJEmSpLmVqprOD04OBO5TVU9qtx8D3L6qnjH2mB+0jzmr3f7P9jG/XvCzDgYObjd3Bn48ldBwPeDXyz6qn4aafai5YbjZh5obhpt9qLlhuNmHmhuGm32ouWG42YeaG4abfai5YbjZh5obppt926raarE7pjoH78pSVW8H3j7t35PkxKrac9q/ZxqGmn2ouWG42YeaG4abfai5YbjZh5obhpt9qLlhuNmHmhuGm32ouWG42YeaG7rLPs0hmmcDW49t37Tdt+hjklwFuBZNsxVJkiRJ0gpNs8A7AdgpyfZJNgUeARy54DFHAo9tbx8IfLGmNWZUkiRJkubc1IZoVtXaJM8AjqVZJuHdVXVaklcAJ1bVkcC7gPcnOQP4LU0R2KWpDwOdoqFmH2puGG72oeaG4WYfam4Ybvah5obhZh9qbhhu9qHmhuFmH2puGG72oeaGjrJPrcmKJEmSJGm2pjlEU5IkSZI0QxZ4kiRJkjQnLPAkSZKkBZJskuQvus4hrZQF3kAlOSnJ05Ncp+ss0rR4nGslkqzpOsNqJdk2yT3b21dPcs2uM6mfPFZmp6ouBd7cdY6NURpbL/9ILcYCb7geDtwYOCHJh5LcO0m6DjWJJK9LsmWSqyY5LsmvkhzUda5JJDmkzZ4k70pycpJ7dZ1rOUnumOQa7e2Dkvxjkm27zjWBQR3nSU5N8v0NfXWdbxIDL6p/kuT1SXbpOshKJHky8DHgbe2umwKf6izQCiS5fxI/S8zIkI+VATsuyUP6/N6zlCQ3aD+vHN1u75LkiV3nWk67bNpRXedYqSRrkjyn8xwbaxfNJKcCi/3Hh+a42nXGkValfWO9H/AW4BLgPcAbq+q3nQZbQpJTqmr3JA+iyf5c4CtVtVvH0ZaV5HtVtVuSewNPAV4MvL+q9ug42pLa4mI3YFfgvcA7gYdV1V27zDWpoRznY0Xz09vv72+/Pxqgql4w81ArlORmwONpiusTaf7WnxvCGqXtlYxH0OTfBHg38KGqOq/TYMtIcgqwF/DtqrpNu+/Uqrp1p8EmkORfgTsAH6dZDulHHUda1pDf/wd+rLwO+DvgD8AxNO9Hz6mqf+002DKSnA9cA1gL/JF1x8mWnQabUFvYvQc4tP38chXguwM5Zo4A3lRVJ3SdZSWSfKeq9uoyw9TWwRuA+3Ud4IpKsivNB5n70ry5fgC4E/BFYPfuki1rdNztD3y0qs4d0ImxUdD70hR2pw3krN7aqqokD6B5sXzXEM7gwbCO86r6OUCSfUcfvlovSHIy0PsCr6rOAA5N8mKa18l3A5ck6WVRPa6qzgfeAbwjyV2BDwL/lORjwCvb/7Y+uqiqLh69lLQfwHpfUANU1UFJtgQeCbw3SdF8mPy39v9HH43e/wN8lua1ZSgGe6wA96qqv2lP7v4MeDDwFaDXBV5VDX0I7PWq6iNJXgiXrVN9SdehJnR74NFJfg5cwABOwrS+nuRNwIdpcgNQVSfPKsBGW+CNPohBc/kauF27+Z2q+mU3qSaX5CTgHJrF4l9QVRe1d307yR07CzaZzyT5Ec1ZvKcm2YrmrNgQnJTkc8D2wAvbKwaXdpxpEue3L+4HAXdpr4hdteNMyxrwcZ4kd6yqr7cbf8GAhsQPqagel2YO3v402bcD/oEm+51phvrcvLNwSzs+yd8CV0+yL/A04NMdZ5pYVZ3XFtFXB54NPAh4XpLDqurwTsMtYsH7/0Xj2wMw5GNlsCd32yHrOwGbjfZV1Ve6S7QiFyT5M9oTAUn2Bs7tNtLE7t11gFXavf3+irF9Bdx9VgE22iGaI0keBrwe+DLNmYE7A8+rqo91mWs5SXaoqjMX7Nu+qn7aVaaVSHJd4NyquqSdG3bNqvpF17mW0xZGuwNnVtU57YvmTaqq1/OrktwQeBRwQlV9Nck2wN2q6n0dR1vSUI/zJLelufJ1LZrXld8BT5jl2bvVWlBUf3ysqCbJJ6rqwV1lW06SM4EvAe+qqm8suO+wqnpWN8mW1r6uPBG4F83xcizwzoEMi30A8DjgZsD7gCOq6pdJNgd+WFXbdRhvWUlO7vsQ+3EDP1ZeAzyQ5uTuXsC1gc9U1e07jLWsJE8CDqGZ73gKsDfwzaqa2Yf1KyLJHsDhwK2AHwBbAQf2/XMLQPtZ5XKq6r9mnWVoLPCS7wH7jq7atVeTvtD3+WCLvSklOamqbttVpkm1b/zPBbapqoOT7ATsXFWf6TjaBrUvkBs0hA/uQzTk4xwgybUAqmooZ0sHW1QDJLlTVX1twb7LrqTqytfOkXnXYlczktyjqo7rINaSFryef4B2juyIr+fTM8STu+2czdsB32r7B9wCeFWfT3Yt1A7l3ZnmpMCPq+pPHUeayNh82dBcPd2eJv8tOw22jCQvWWx/Vb1isf3TsNEO0RyzyYIhmb+hx0Op2heWWwLXSjL+4rIlY0MHeu49wEnAaG2Zs4GPAr0t8GiGem3ITC+7r0Q7OXypZgK9nCQ+1OM8yUFV9a9JnrtgPwBV9Y+dBFuZjwELT2h8DBhCUX0Yl89++CL7emGJZh8ADGCeCcAvFhZ3SV5bVc/vY3HXGn89/wXwhvZ26Onr+TwcK+3J3acB2wAH03RI3pl+v/cD/LGq/piEJFerqh8l2bnrUMtZ8N457uZJqKpPzDTQKixsBNOenHlaR3FW4oKx25vRzPs9fZYBLPDgmCTHAv/Wbj+cfrdl3ZnmQLk2cP+x/ecDT+4i0CrsWFUPT/JIgKq6sO+NSqpqn64zrMaAJ4cP9Ti/Rvt9cH/3oRbVAEnuQHPCaKsFxfWWQJ/Xxhs1+1jYdfUghtM4Y1/g+Qv27bfIvt4YvZ4nuTrNh8U70fy9v0rTqbeP5uFYGeLJXYCzklybZjmKzyf5HTCEeZuj987r0/zNv9hu7wN8A+h9gbdQVZ2cpNdDegGqar2LAkneQDOcemY2+iGaAEkeAowaNny1qj7ZZZ5JJLlDVX2z6xyrkeQbwD2Ar1fVHkl2pOm41mlL2UkluRWwC+tPtu71XLaRJNdn/dy9Hsc+5ON8aNq5VA8EDgCOHLvrfJqlBr6x2PP6IE3HzLsBfwW8deyu84FPV9VPusg1qSTfXdB1tfdzw5I8laY42hEY7056TZrX9t6vbZrkI8B5NMM0oZmnfK2qelh3qZY2xGNlJMmJVbXn+H9D2qWHus42qfa15lrAMVV1cdd5JtE2hntsVf1vu30j4L1V1fsGJgtO2G1CMxrjz4aQfVzbpOeEqrrZrH6nV/CAqvo4Tae43kvyN1X1OuBRoytg4/raRGCBl9GsgbN1kg/QFNeP7zTRhJK8lOaD5C40V3r3A75G01ygt5IcQDMs6cbAL4FtaYYL9HIc+9CP83Ze0iFVdU67fR3gH6rqCZ0GW0JV/Tvw70MsqqvqeJrugu8dWEfEkWR4XVc/CBwNvJr1l/84v3q8lMYCt6qqXca2v5Tkh52lmcwQj5WRi9urpqNujjsCFy39lO618wZHTm2/D+nqyNaj4q71fzTDZIdgfDTMWpplTXr/eX3BkOo1NI1tZjb/DizwRmOUX0tzCTv0fG4S68bwnthpiiugqj7Xdurbm+bvfUhV/brjWJM6kGbB8O9W1ePTLLHR6zV8Wq+k+Xt/oapuk2QfmqE9fTX043zXUXEHUFW/S3KbJR7fuSEX1Un+uaqeDbwpzTps66mqA2afakWeCLy7bcpzWdfVbiMtq6rqZ0mevvCOJNcdSJF3cpK9q+pbAO3Qr76/5gzxWBl5KZc/ufu4ThNN5mRga5q/dWimDvwiyf8BT66qkzrMNonjFpmK9IUO8ywryfur6jHAOVX1xq7zTGqsIdn4Wttrgf+rqrUzzbKxD9FMcgZw/6qa6eTHjVmS46rqHsvt66Mk36mqvdoCdR+aIWCnV9UtOo62pLGhMd8DblNVlw5taMyQtH/nu1XV79rt6wLHL5ww3idJ7l9Vn07y2MXur6ojZp1pUkluW1UntcOnLqe9wtd7GVDX1SSfqar7Jfkp67rcjVRV7dBRtIklOZ1mvu9oqPo2wI9pPpBVnxuXDOlYGZdmaaHRyd1vDeHkbpJ3AB+rqmPb7XsBD6GZU/jG6vkyD3DZxYw7t5tf6ftUpPZK+j1pRgncjfVfX+jrCaS0Xb778Jl2o7+CR1NVD6a4S/Jplu6k1dsz1Uk2AzYHrtcOWRv9g90SuElnwVbmxHay9TtoJov/HhjCcLZzkmwBfAX4QJJfsn6Xp14Z8nHe+gfgm0k+SnOcHwj8fbeRllZVn26/97aQ25DRGfShFHIjGXDX1aq6X/t9+66zXAH36TrApIZ8rOTyywyNhgtuk2Sb6v+yFHtX1WXNvdpRSG+oqqckuVqXwSbVdswcUlOVtwLHATvQfNZa7wRSu7+PNknytzSdSp+78M5Z/jvdaAu8sS5xJyb5ME13pMvGgve4feyonfODgRuybnjgI2nGVffZU4Bn08wDG/8Hex7wpo4yTazt9PnqdujdW5McA2xZA1gsFHgAzeKyz6FZ8+lazHg8+AoN+Tinqt43dpUX4MFV1eu5PUMuqjPcFvKD7bo6kuSOwClVdUGSg2iaIPxz3xs4AQxsvuaQj5VRR8HNgD2B79G8/+9KMyT2Dh3lmtT/Jnk+8KF2++HA/yVZA1zaXaylJflaVd0pl18uqe9Tkaiqw4DDkrylqp7adZ4VeARNs7Kr0PG/1Y12iGaS9yxxd/W5GQKsG3K33L4+SvLMqjq86xyrkeTUPg+zW0z7JvSFGuBSD0M+zmFYXUvHhjcuWlRX1XM6CTaBJNu2NxdtIV9VL7j8s/otyaZD6NKX5Ps085J3Bd4LvBN4WFUtOlxWV74BHSufAF5aVae227cCXlZVB3abbGlJrkczf/BO7a6vAy8HzgW2qaozNvRcrc6CxjaX09chmiNJ9quqozvNsLEWeEPXzh3Yv6rObLe3B46qqj/vNtlkMtClBtruiG+qqhO6zrISSY6juYo0tPkagzzOs4GupVXVy66l44ZcVGegLeSTfBl4XFX9rN2+HfDOIcyRHf19k7wEOLuq3jWEv/lQDfxYOW3ha+Bi+3TlSnLPqvrCgn2P7fNw/LG5vbBg/h09nuO72LDMcQ7RnKEkNwUOZ2wdPJqujmd1l2oizwG+nORMmoN/W5ohkL2XgS410Lo98OgkP6eZwzYa6tDXIWAjvwdOTfJ5xubeVY87I7aGepwPrWvpuGsk2WFBUX2NZZ7TF8kwW8i/GjgmyWE085H3YyBLxwDnJ3khzfF9lySbAFftONM8G/Kx8v0k72Td6IBHA72f4pDkSywyBLyq7t5BnNV4SZr1nv8fsAXNVfaLgN4WeAOe29ubIdQb/RW89gPvB1l/SM+jq2rf7lJNpp3cO+re+KOq6v16MnDZfJnRUgO7pV1qYCB/820X29/3uRxD7Iw4MsTjfMhdS5PcB3g7sF5RPeog12dJbgu8m2aO6WUt5AfQxIEkdwM+D/ya5pj5RaeBJpTkhjQLhJ9QVV9Nsg1NB9khnLAbpAEfK5sBTwXu0u76CvCWqvpjd6mW176ujGxG00FzbVX9TUeRVqTtH/DXrDs5+pKq+rclntIbSe6y2P6q+sqsswyNBV5ySlXtvty+vkhy96r64liTmPX0uDnMZTLQpQbGDWlu1UiaBWa3qaofd51lOUM/zpN8gWai9auB69EM07xdVf1Fl7kmNcSielwG1kI+yYuBhwEH08xlew7w11X12U6DqXc8Vvph9Dmm6xyTaOezvZWmY/lNaa6gvrYGUAC0zb9GNgP2Ak7q+9XTts/HYld9Z9bfY6Mfogn8pu38NTqb8UjgNx3mWc5dgS8C91/kvmIYbXCHutTABudWAb2eQ5Dk/jSdKTcFtk+yO/CKHndGHPpxPrSupUsV1Tsm6XVRnQG3kG/9GbBXVf2BZnmNY2iGUfX+Q3t7vLwWuD7NVdPed+gbuMEdK0k+UlUP21C3275PcVjQ8GMT4LY0r+lD8S3gNVX17vZE72tpGsX0/oRjVa33GSDJ1sA/d5NmRT4zdnsz4EHA/8wygFfwmiF3h9O06S3gG8CzhnBFZh4k2Y7hLDUwWsD67iyYW1VVT+w42pLaq6V3B748akKR5AdVdatuk6kvkry8ql6axTsM97qzcJKnVNXb2vm9l1NVL591ppVqh6rfrt38TlX9sss8k0pyBnD/GtB6skM3tGMlyY2q6n8HPMVh1PAjwFrgpzQnSL/WabAJpVlr8L8W7LvLEIc5tsNNT6uqXbrOshLt3OSvzXIUz0Zf4A1VklcBr6tmTTbSLBz+11X1ok6DTSjJTWiufl12FXkILzZDnVuV5FtVtfd4l8Ek3x/AmdNBH+fSJJI8lOYK+5dpPkTeGXheVX2sy1yTSPL1qrrj8o/UlWGox0oGvFzP0LVD1l9KMzKmgONpCtTeD2FPcjjrrvpuAuwO/KyqhtK0DIAkOwOfraqbzep3DqG72FQlOaIdLjjavk6Sd3cYaVL7jT70AlTV74D7dhdncklGwwNeBDyv/fp/nYaa3DlJtqDptvqBJG9krCtlj52W5FHAmiQ7tS+a3+g61AQGe5wPVZJXLfKa+HcdRprYgF/PX0QzR/OxVfWXNPNMXtxxpkmdmOTDSR6Z5MGjr65DzbFBHitVdQlw6Wh+7JAkeWiSa7a3X5TkE0mGtAzIu2l6HTyUZv7mecBSa0H3yYk0U3lOopnK8/whFHdJzk9y3ugL+DTw/FlmcA4e7LrwA2SS2yzx+L5Yk+Rqo+YH7bjqq3WcaVIPBHYeWuOG1mhu1bMZyNyq1jOBQ2laI/8bcCxNK/++G+RxnuQawB+q6tJ2exNgs6q6sNtkE9mvqv52tNG+Jt6X5oNl3w319XyTBcPsfsNwTsBuCVwI3Gts3xDmyQ7VkI+VoS7X8+Kq+miSOwH3BF4PvIVm2aQh2LGqHjK2/fIkp3QVZiXGO323I3i27jDOxKqq8+USLPBgkyTXaa8MjCbTDuHv8gHguLH5Mo+nx2uaLHAmzTpJgyvwquqCdh7BTlV1RJLNgTVd51pOW1gc2n4NyVCP8+NoPgj8vt3eHPgcA5jUzkCL6tZQX8+PSXIs65p9PZxmjdDeq6qhrME2LwZ7rNAU/UMs/C9pv+8PvL2qPjuUUQ2tPyS502jOYJI70pyo7r0kXwYOoHkdPwn4ZZJvVNVzOg22jPZvfEr7mfEgYA/gjbOcb7rRz8FL8pfA3wIfpRnPfiDw91X1/iWf2ANJ9gPu0W5+vnq+TtXYWOqb0KyDdxxjRd4AzuKR5Mk07amvW1U7JtkJeGtV3WOZp3Yqyc1phsFux/rzHnvdahiGd5zD8JZfGZfk+TTdS8eL6iOr6nXdpZrMwF/PHwKM5rJ9tao+2WWeSbWvLW8BblBVt0qyK3BAVQ3pA/CgDPVYAUiyKXDzdvPHVfWnLvNMIslngLOBfWk+qP+BprlNr+fej6Tpmn0E69YH/S3wuKr6Xpe5JjHqG5DkScDWbSOwIfQP+D7N59xdgffSdLp9WFXddWYZNvYCDyDJLjQdBgG+WFU/7DLPvMoGFtseqWEsun0KzZyHb481Kzm1qm7dabBltE1h3kpzBmx0NpKqOqmzUHMsydeBZ1a7wHaahXLfVFV36DbZZIZYVI8kuSXN+prg6/nUJTmeZh712+zQq6WkWaD9COBnNIXG1sBj+95grR2pcx/g1Kr6SZIbAbeuqs91HG1FkmwJUFXndZ1lUmmW1rgXzXFzaFWdMJAC7+Sq2iPJS4Czq+pdo32zyjCEoStTkfXXNfkF8MHx+6rqt7NPNbkke9Ms7/DnNGubrQEuqB6vPbRgLPWmNAspF81ZvIs7C7YyF1XVxWnX10pyFRZZ16eH1lbVW7oOsVJDPM5bzwY+muR/aD7I3JBmKNUgVNXRwNFd51iNqjotya9o1h5atEV4XyT5WlXdKcn5rP86MqS15Davqu+MXhNba7sKM6/m5Fj5B+BeVfVjuOzq77/RrCvXW+0Uh0+Mbf8v8L/dJZpMhr8+KDQ9Do6lWWLghCQ7AD/pONMkzk/yQuAg4C7tPPyrzjLARlvg0VzJGK1rAuteMNPe3qGLUCvwJuARNEOR9gT+knXDHnqtbdjwNuA/af7e26dZx2oIHyiPT/K3wNWT7As8jaY7Ut99OsnTgE+y/rDYXp/IYKDHeftGdAtg53bXIIYiwaCLapIcQPMh8sbAL2mWYjkduGWXuTakqu7Ufu98Qv4V8OskO9K+hyY5kAF8+B2aOTlWrjoq7gCq6j+SzPRD70bmGu33wR4zVfVRmvf/0faZwGUNY5K8sKpe3UW2ZTwceBTwxKr6RZJtaJrzzIxDNLnsat5OtGd8Aarq+O4SLS/r1mO77FJ1xtY467MkPwLuV1VntNs70qwPcotuky2vPQvzRJohA6E5s/TO6vk/pDQLtS5UVdXrExlDO86T3L2qvpgNtImvqt43GEhyIosU1VX1wk6DTaAdinx3mvW2bpNkH+Cgqnpix9EWtWAkyeUM4AQM7Rn1t9M0EPodzSLQj55lM4GNwZwcK+8GLgX+td31aGBNVT2hu1QaslkPe7yyJPnmtKdsbMxX8ABoJ24eAtwUOAXYm2Z9sF43zQAubIc5npLkdTRnTIfSKvn8UXHXOpNmjZbeq6bt/Tvar8Goqu27zrBKQzvO7wp8kaZJyUKDaR1fVWckWVPN2lXvSfJdoPcFHvCnqvpNkk2SbFJVX0ryz12HWsL4SJJtaAqkANcG/gvo7b/bBcO+jgK+RPNv8wKaM+xDGP41JIM9VsY8FXg6MGqo9lXgX7qLM9+SHLbU/UNobDeBLP+QXtps+YdcMRt9gUdT3N0O+FZV7dMOq3pVx5km8RiaN9NnAM+hmaz8kCWf0R8nJjkK+AjNG9ZDgRNGVz36fJWjbX37MpqhX1dh3fyHXl4Jm4MrSoM6ztsOX5sAR1fVR7rOs0pDK6rHnZNkC+ArwAeS/JKx9bb6ZnTiJck7gE9W1VHt9n4064X22WjY184076H/TvN6+BjgO12FmlcDP1YAqGbplX9kIMX/IvMdL7uLYcx7HDVRuyOwC/DhdvuhwLw0n+r16KklTD33Rj9EM8kJVXW7tjvi7avqoiSnVVUv52zMg6xb02wx1efhGu3w0udw+W6Uv+ks1BKSvLwtOhb7m/f6bz1ko6GlXedYjTTrPP4fzfy759C01v6XBVfdeyntAvM0BemjabJ/oK//PkcW68Q7hO68AEm+AuxfVee329ekGXJ/l26TzachHittJ8QNftjse0fEoUvyLeBOVbW23b4qzfIae3eb7Irr85SNpcxiaKlX8OCsJNcGPgV8PsnvAOcOTFENe2HccwfSDAZorii134f8Nx+iLyT5fzRnTC+7gjSEeTJjc6f+CLx84f1JPl5VvbyKWlWjv/WlNG211zOLeQ+r9D9JXsT6c5P+p8M8K3EDYLwL8sXtPk3HEI+V+3Ud4MqQ5Pqs36uhl915F3EdYEua9e8Atmj3zYOPLv+QXpr60NKN/greuCR3pTnje8yA2vYPRtYtdL6oIYwHT/Iamq6Cn2D9bpQndxZqAu1JjL/k8gud9/5vPkRDbWoziaGeMYX+Zm8baLwUuAvNa+RXgFcM4YRAkkOBh9F06IVmuOCHe9rZbvCGfKwM1Ya68w5lpFeSx9NMLfkSTWFxF+BlNYy1h28OvAW4QVXdKsmuwAFV9XcdR1tSktdW1fM3tC/JrarqB1PNYIE3bEk2r2aNlt7LfCx0/qVFdldV3X3mYVYgyTeAbwGn0lzdAIbxNx+iJJtV1R+X2zdEQ+1aBsPO3mdJ9gDu3G5+paq+22Ue9VM7F/y1wPVpCo1BzGUbWnfexSS5IXD7dvPbVfWLsftuWVWndZNsaUmOB54HvG10ci7JD6rqVt0mW9pi7zWZ8QLtDtEcqCR/AbyT5lL7Nkl2A55SVU/rNtmGzUMxUVX7dJ1hlTarqucu/7B+SPJplr7ae8AM46zGN4CFhcRi+6TBa0cw9HoUg3rhdcD9q+r0roOs0NC6815OW9D9+wbufj/9fW/avKq+k6w3onFtV2GWk+SpNOsj75Dk+2N3XRP4+iyzWOAN1z8B9waOBKiq7yUZxKT29irY5T689/kqWJKDqupfF7QGv0xV9b0r2PuTPBn4DMNY6PwN7fcHAzdk3XyTR9I0AOml9izpTYCrJ7kN68bZbwls3lmwK9dQ21LDsLNLQ/d/AyzuYGDdeVehz6+Lv06zVnIBJDmQprNzX30QOBp4NfCCsf3nz/rzlgXegFXVfy84q3HJhh7bM/9v7PZmNG3ve3tGpnWN9vs1l3xUf10MvB44lHXFdQG9nBNWVccDJPmHBd0oP90uxN1X9wYeR7Ou5njRfz7wt10EmoLnL/+Q7rRdQHeqqi8kuTpwlVGHR5oW/r2SZA3wrKr6p66zqN+GeqyMLdNzYpIP0zS1Gz/R2Pfleh5A03TqOazrzvuKThNdufo8V+vpwNuBWyQ5G/gpcFC3kZa0BjiPJvd6klx3lkWec/AGKsnHaD5AvolmXPUhwJ5V9YhOg61Sku9U1V5d55hXSc4E9qqqX3edZSWSnE7Tgv3Mdnt74Kiq+vNuky0tyUOq6uNd51iJJVqZj+bJ9L6VeXuV+mDgulW1Y5KdgLdW1T06jrYkX/80qSEeK2PL9IwWah/ncj0dG8Lc5HYJnE3GTtb1UttgbfQ+utixPrOT6l7BG66/At5IMxzsbOBzLHLGoI/aLmAjmwB70pwR6722wHgml+9G2fc5YWcAg2jGs8BzgC+3BWpoupc9pdtIGzYaygtst9hw3p4P5Z2HVuZPB/YCvg1QVT9pW5v33deTvInLL6vhvDYtNLhjZbRMT5IjgEOq6px2+zo03Sl7bcGC55sCVwUu6HtzGIA0w7xuWlX/vcTDets1PskNgFcBN66q/ZLsAtyhqt7VcbRFVdX2XWcYscAbqPZKzKO7zrFKJ7HuTN6fgJ8BQ+lG9SngXcCnGetGOQAXAKe08x/Hh8b0epmEqjqmvQpzi3bXj6rqoqWe07HRUN4tOk2xCmPr3w3ZRVV18WjoepKr0O/hRyO7t9/Hh30VTec+adzu7fchHiu7joo7gKr6XTtXudeq6rKpGW3B9ABgEIuEV1UlOQq49RKP6fN/y3uB99BMLwH4D5qTG70s8Ma1JzB2Yv21E78yq99vgTcw87CWHM0cnmOq6rwkL6bp3jSUq0t/rKrDug6xCp9qvwYlyebAc4Ftq+rJSXZKsnNVfabrbIupqre13y+3QPhQJNkbOBz4c5qz1WsYyNlq4Pgkf0vT5GZfmm5mn+4407IG3J1XMzbwY2WTJNepqt/BZaN5BvU5tJp5TZ9K8lLWb6LRZycnuV1VndB1kFW4XlV9JMkLAapqbZLe95tI8iSaqVM3BU6hOSHwTWZ4ImZQ/7AEQJ8bTEzqRe0/2DvRHOxvoFnI8vZLP60X3ti+sH+OAS10vtwSFUk+XlUPmVWeFXgPzRXfO7TbZwMfpekG2lsbGoo0kLkmbwIeQfN33hP4S+DmnSaa3AtoRgOcSjOU9yia5WR6LclLFttfVfPUyEFXgoEfK/8AfDPJR9vthwJ/32GeiYw1iYF100qGtKbp7YFHJ/k5zWiewcyrBi5I8mes66K5N3But5EmcghwO+BbVbVPklvQDDWdGQu8gZmHteRY1+1zf+AdVfXZJH/XZaAVuDVNJ767s26I5lCGxyyll900gR2r6uFJHglQVRdmQevYnhrkUKSRqjojyZqqugR4T5LvAi/sOtdyqupS4B3t15CMt1zfjGY+5BDbyWv6BnusVNX72i7Io/fLB1fVD7vMNKH7j91eSzOt5AHdRFmVe3cd4Ap4Ls1yYDsm+TqwFXBgt5Em8seq+mMSklytqn6UZOdZBrDAG5gk/1xVz97QQtADaPYBcHaStwH7Aq9NcjWas2JD8FBgh6rq7aTkVerrPKWL21b3o7N3OzJ25bTHhjwU6cIkm9LM2XwdzZpDg/j3uYFOoOfSjHz4u6r6zexTLa+q1ms0keQNwLEdxVGPDf1YaQu6IRR1lxk1iRmwvr6/L6ldFuSu7dfONFcef1xVf+o02GTOSnJtmqkxn0/yO2Cm89yH8oFD67y//f6GJR/Vbw8D7gO8oarOSXIj4HkdZ5rUD4BrA7/sOMfG4mXAMcDWST4A3BEYwpvtIIcitR5DU9A9g6aL6dY0C84PwdE0IwQ+2G4/gmaB+V/QTNa//+JP653NaeZuSMvxWJmSOel5APBZ1jW22wzYHvgxcMsuQy2nqi5J8sh23cfTus6zElX1oPbmy9rmdtei+SwzMxZ4A1NVJ7U3d6+qN47fl+QQ4PjZp1qZqroQ+MTY9v/SXCUYgmsDP0pyAuvPwRvCldOl9HLYY1V9LslJNBOUQzOvrfdr+Q14KBLAA9vXlj8CL4fLXlveuOSz+uGeC9ZzOnW0xlOS3i6Ou+DK4xqaYUhDmFOlGfNYmalRz4M7ArvQdG+E5oTdUF7Pqar1Omgm2YOmAdUQDG5ZELhstNFZbdfv0CyttTkzXJLChc4HarGFKZN8t6oGM89niJLcdbH9VdX7wnopSe5VVZ/rOsdCSd4PPKOqzm23twXe3feFq4dsyK8tSb4HPLmqvtNu3w54Z1Xt1sf/hiTbV9VP2+N6ZC3wf1W1tqtc6h+Ple4k+RZwp9HfOclVga/2fHmBJSU5dWHh10ft1a+Fqqp63fcgySk0zXi2o2n29e/ALavqvrPK4BW8gWmbTTwK2D7JkWN3XRP4bTepNh7LFXJJvllVd1jqMbO0gTlJsKCLVh+Lu9bXgG+3i4bfhGYo7193G2k+LfHasiXDeW15EvDuJFvQHOPnAU9Kcg3g1Z0mW9zHgNviSQstz2OlO9dh/dfBLdp9g9C+f45sQrM01f90FGciSQ5pR5K8uKq+1nWeVbi0XdLhQcDhVXV426xsZizwhucbNMMZr0czz2fkfOD7nSTSuM2Wf8hM3a/rAFdEVb0tyWnAl4BfA7epql90HGteDf61pV3n6dZJrtVuj7fT/kg3qZa0Sbtu380XfAgDoKr+sYNM6iePle68BvhuezUpwF1o5ocPxTXHbq+lmZP38Y6yTOrxNNMCDqMpSIfmT+1J08eybu73VWcZwAJvYKrq5zSdeHpzlUjr6dWY5/Z4GawkjwFeTLMW267AUUkeX1Xf6zbZ/Bl/bUlyA5o1fABOH9IQsCT70zQP2Gy0okaP1wh7BPBAmvfiay79UG3kPFY6UlXvSXI069bqff4QTjQmeX9VPQY4Z2HPhgE4PclPgBsnGT/BOJQ1/B4P/BXw9+3Q6u1Z1yRxJpyDN1DtYo+HA38ObEoz2fqCqtqy02AbucXmL/XBUI+XJJ8CDq6qX7bbewFvr6rdu8w1z5I8lKZL75dp3kzvDDyvqj7WZa5JJHkrzUT2fWgWOD8Q+E5VPbHTYMtIsl9VHd11DvWfx8rsJLlFu37Zou/pA2j08UPgnjTdhe/GgmZqVdXrofdJbkizBMjlmtjNwcnrj1fVQ6b6Oyzwhqnt0PcI4KM0Ezn/Erh5VfV+MeJ51sdGDjBfx0uSTedwHcLeaBuV7DtWVG8FfKGqdus22fKSfL+qdh37vgVwdFXduetsi1lsqN04h91pxGNl9pK8vaoOHnCjj2cBTwV2AM5m/QKvqmqHToJdiWZRKE3DLD4rOkRzwKrqjCRrquoS4D3tBM7BfWAfmraL2U5V9YV2Ee6rVNX57d2P6TDakoZ0vCT5m6p63RLrEA1l/aEh2mRU3LV+w0AWOqdZ2gGaxdpvTJP9Rh3mWY5D7TQpj5UZq6qD2+/7dJ1lNarqMOCwJG+pqqd2nWdKhlqkTv3qmgXecF2YZFPglCSvo2mOMJQPYYOV5MnAwcB1gR1pFph9K3APgKr6QXfpljS042W0xtCJSz5K03B0kmOBf2u3H07T5nkIPp3k2sDrgZNp3kTf0WmiJVTVy7vOoGHwWOlOO2z9mKo6P8mLaJp+vLKqZtoVcaWSXLe9eejY7cv0fYjmhByGuAEWeMP1GJp5VM8AngNsDQzuMvUAPR3YC/g2QFX9JMn1u400kcfQFHTjx8uDO020tIcDnwGuPcDJ4UNXwNuAO7Xbb6dZaL7XkmwCHFdV5wAfT/IZYLMFnTR7Kcl7WOSDSlU9oYM46jGPlU68uKo+muRONHPaXk9zYvf2Sz+tcyex7ljJgvuK4V79mgcL/39c6SzwBmpsgukfAM/szc5FVXXxqDtfkqswjDNID2wLpT/SHi9JDqFpQ9xHt22H2D0hyfsY2OTwgdu3qp4PfGK0I8nLged3F2l5VXVpkjcDt2m3LwIu6jbVxD4zdnsz4EH0fJ0qdcZjZfYuab/vT9Pk67NJ/q7LQJOoqu27zjADUy+UVqudwrNNVf14kbun/n5qk5WBWWLhagAG0Dp20NrhjefQNCl5JvA04IdVdWiXuZazWHfPvjaEgY1jcnjfJHkqzfG8A/CfY3ddE/h6VR3USbAVSPIG4JvAJ2rAb27t1civVdVfdJ1F/eaxMn3taICzgX1phmf+gaY7b+8bTwEkucti+6vqK7POshpLFUpJ7lVVn+sg1pKS3J+mG/WmVbV9kt2BV1TV5TqCTi3DgN8DN0ptg48NGnrr2L5r30yfCNyLpug4FnhnXz9MtgttPopmuN1Xx+7aErikqu7RSbAJzfnk8F5pFwe/DvBq4AVjd50/lCumSc4HrkFzxv0PrFszqdfLgSyUZGfgs1V1s66zqN88VqYvyebAfYBT22kZNwJu3cfCYjFJPj22uRnNNJOT+t4FFPpRKK1GkpOAuwNfHp1IT3JqVd16Vhkcojkw4wXcgsWIv7Og852moKoupWna0NvGDQt8g6ahyvWAfxjbfz7w/UWf0SMWd7PTzlU7F3hk11lWq6oG2WmwLUzHTxL9gp4PiVU3PFZmr6ouTPJLmhOlPwHWtt8HoaruP76dZGvgn7tJs2IvoylIvwxQVae0i4b33Z+q6tzRdJ7WTC8EWOANVJKH0Uz0/TLNWerDkwxiMeIh28AQ2XNpuj3+XVX9ZvapNqw9IfBz4A4LTgicXlVru0smXfnSvJs+Gti+ql7ZfpC5UVV9p+NoSxpqYarZ81iZvSQvpVk/dmfgPcBVgX8F7thlrivgLODPuw4xoc4LpVU6LcmjgDVJdqJZ2ukbswxggTdchwK3W7gYMWCBN11H0wz/+mC7/Qhgc5qzqO8F7r/407rVtnl+A54Q0Hz7F+BSmqExrwR+D7yZdSc2einJHYFTquqCJAfRzPN5o0PutZDHSiceRNO86WSAqvqfJIMptBesJ7sJsDvtf8sAdF4ordIzaT6nX0Sz5NCxNO9JM+McvIFaOJa3nRv2vVmO790YbaBZyclVtcesx1evRJLv0XRHXO+EwFAmiUuTGPu3eFkDoSTf6/txnuT7wG7ArjQnit4JPKyq7tplLvWPx8rsJflOVe019vpyDeCbQ2lql+SxY5trgZ9V1de7yrMS7fzHQ1m/78Erq+qPnQYbAK/gDdcxA16MeMjWJNlrNOQrye1o1iOE5oWzrzZZMEfzN/R7oXNpNf6UZA3t2er2RMal3UaayNqqqiQPAN5UVe9K8sSuQ6mXPFZm7yNJ3gZcO8mTgScwnHn4VNURo9tJrkOzDu4gVNWFNAVerzuVL9Q2ttnQdJ63zaJAtcAbqKp6XpIHM7YYcVV9sstMG4knAe9OsgXN2aTzgCe1Z/Re3WmypR3tCQFtBA4DPglcP8nfAwcCL+o20kTOT/JC4CDgLu2IjKt2nEn95LEyQ+283g8Dt6B5v98ZeElVfb7TYCuQ5MvAATSf+U8CfpnkG1X1nE6DTaAPhdIqnQlsxfqfuc4Hbk5zcuAx0w7gEM2BSvJc4MNVdXbXWTZGbUv5UefB3kvyWuDbrDsh8FVg73ZBa2luJLkFcA+aEzDHVdXpHUdaVpIb0ixnckJVfTXJNsDdqup9HUdTz3iszF6fp19MYjRkPcmTgK2r6qVJvj+EIaZJ3sjlC6XzaIq+Latq6oXSaiQ5oaput9i+JKdV1S2nnsECb5jark4PA35Lc3bpo1X1f92m2jgk2R+4Jc16MgBU1Su6S7S8DcwdHMQLvDSpJIcBH6qqIUzClzQASY6gGQ57QtdZVqPt/n0v4Ajg0Ko6YSjv/30olFYjyenAvavqv9rtbYBjq+rPx+eIT5NDNAeqql4OvDzJrjRnNI5PclZV3bPjaHMtyVtpumbuQzO5/UCgty3YkzwVeBqwQzs5f+SawCAmWUsrcBLwonbx50/SFHsndpxJ0rDdHnh0kp8DF9CMDqghFEitV9A0J/laW9ztwHDW8dsiyTYLCqUt2vsu7i7Wsv4a+FqS/6Q5XrYHntZO5zliyWdeSbyCN3DtcI2H0rTrv+aAXnAGaXTWa+z7FsDRVXXnrrMtph1Keh2a+YEvGLvr/Kr6bTeppOlKcl3gITSvi9tU1U4dR5I0UEm2XWz/vCxNkeSFVdXLHgJJ7gu8FVivUKJZ8unJVfXPnYVbRpKr0czdBPjxrOcLWuANVJKn0QzR3Ar4KPCRqvpht6nm31i75G8BD6bpRnlaVd2s42iSWkn2ohnZ8ADg9Krq5fqU0mokuTrNiYsfd51Fw7fYFI4+6bpQWq0ktwJ2Yf3pPDObK+sQzeHaGnh2VZ3SdZCNzKeTXBt4Pc1CocWA2iVL8yzJ62gWJf5P4EM06yWd02moJbRzYxY7yzq0IWCakST3B94AbApsn2R34BVVdUCnwTRk6TrAMnai6V66GbBbkpkWSqvR9sm4G02BdxSwH/A1YGa5vYInTahtR733qIFDe1Zps6F00pTmXZKnAJ8AdgCuNtpfVV/pLNQSNjT0a2RehoDpypPkJODuwJdHjRqG3uVR3erzFbwNFUpVdWCXuZbTnrzbDfhuVe2W5AbAv1bVvrPK4BU8aUJVdWmSNwO3abcvAi7qNpWkMZcCXwRuCpwC7A18k+YDce9YwGkV/lRV5zbLs13GM/W6Ivp8Be9A1hVKjx8VSh1nmsQf2s+Ma5NsCfySGS8wv8ksf5k0B45L8pAseHeV1AvPAm4H/Lyq9qE5GXNOp4kmkGTvJCck+X2Si5NckuS8rnOpl05L8ihgTZKdkhwOuCyIroiPdh1gCX+oqkuBzgqlVTqxnc7zDpruzifTnGycGQs8aWWeQvNieHGS85Kc7wcxqTf+OJqAn+RqVfUjmrkbffcm4JE0rcuvDjwJeHOnidRXz6RZh/Ui4IPAucCzuwykfkty8yTHJflBu71rkheN7q+qV3WXblmdF0qrUVVPq6pzquqtwL7AY6vq8bPM4Bw8SdJcSPJJ4PE0H3jvDvwOuGpV3bfLXMtJcmJV7Tm++PCsFsPVMCXZvKou7DqH+i/J8cDzgLeNzdv8QVXdqttkK5NkO2DLqvr+co/tWpLjquoey+2bJufgSSvQDs18NLB9Vb0yydbAjaqqt4udSxuLqnpQe/NlSb4EXAs4psNIk7owyabAKW0n0P/FETZaRJK/AN5Js9jzNkl2A55SVU/rNpl6bPOq+s6CmSVruwqzEuNFUVX9bOG+vkmyGbA5cL0k12Hd/MYtgZvMMosFnrQy/0LTyOHuwCuB39MMpbpdl6Ekra+qju86wwo8hqagewbwHJo5Jg/uNJH66p+AewNHAlTV95LcpdtI6rlfJ9mRthlPkgNpTiL1Vp8KpRV6Cs0IkhvTDCkd5T6PZij+zFjgSStz+6raI8l3Aarqd+2Zd0larQdW1RuBPwIvB0hyCPDGTlOpl6rqvxdcjbmkqywahKcDbwdukeRs4KfAQd1GWlZvCqWVaF/H35jkmVV1eJdZLPCklflTkjWsOxO2Fc0VPUlarcdy+WLucYvsk/67HaZZSa4KHAKc3nEm9VhVnQncM8k1gE2q6vyuMy2nT4XSalTV4e2/0+0Yq7VmuUC7BZ60MocBnwSun+TvadZoedHST5Gky0vySOBRwPZJjhy7a0vgt92kUs/9FU3hfxPgbOBzNFdopEW1a8e9CrhxVe2XZBfgDlX1ro6jLasPhdJqJHk/sCPNeqyjK+wFzCy3XTSlFUpyC+AeNEMGjqsqz55KWrEk2wLbA68GXjB21/nA96tqEI0QNBvt6JH3VdWju86i4UhyNPAe4NCq2i3JVWgWDr91x9GWtaFCqaqe1VmoCSQ5HdilOiyyvIInrUCSw4APVZVrVEm6Qqrq58DPgTu0Z9lHzZpOt7jTQlV1SZJtk2xaVRd3nUeDcb2q+kiSFwJU1dokQ5m3uScdF0qr9APghnTYzMYCT1qZk4AXJdmZZqjmh6rqxI4zSRqwJA8F3gB8mWZkwOFJnldVH+s0mProTODr7ZDeC0Y7q+ofu4uknrsgyZ+xrnfA3sC53UaaWOeF0ipdD/hhku8AF412VtUBswrgEE1pFZJcF3gI8Ahgm6raqeNIkgYqyfeAfavql+32VsAXqmq3bpOpb5K8dLH9VfXyWWfRMCTZAzgcuBVNwbQVcOBAFgz/ErA70FmhtBpJ7rrY/lku3+MVPGl1bgbcAtgWO5hJumI2GRV3rd/gQucak+T9VfUY4Jy2w6C0rHbe5l3br51pRgj8uKr+1Gmwyb2s6wCrUVXHt3Osd6qqLyTZHFgzywxewZNWIMnrgAcB/wl8CPhUVZ3TaShJg5bk9cCuwL+1ux4OnFpVf9NdKvVJkh8C9wSOBu7GunXBAKgqu65qUUm+U1V7dZ1jtRYrlPq+1EOSJwMHA9etqh2T7AS8taruMbMMFnjS5JI8BfgEsANwtdH+qvpKZ6EkDV6SBwN3aje/WlWf7DKP+iXJs4Cn0rz3nM36BV5V1Q6dBFPvJfkn4KrAh1l/3ubJnYWaUB8KpdVIcgqwF/DtqrpNu+/UWXYudYimtDKXAl8EbkrTtndv4JvA3TvMJGnAkry2qp5Pc/Jo4T6JqjoMOCzJW6rqqV3n0aDs3n5/xdi+YhifW55OWygBVNVPkly/20gTuaiqLk6a8zDt0hQzvaLmGH9pZZ5F08r851W1D3Ab4JxOE0kaun0X2bffzFOo9yzuNKkkh7Q3X1xV+yz4GkJxB22hNNroolBapeOT/C1w9ST7Ah8FPj3LABZ40sr8sar+CJDkalX1I5qJy5K0IkmemuRUYOck3x/7+inQ+w53knrt8e33wzpNccV0Xiit0guAXwGnAk8BjgJeNMsAzsGTViDJJ2leNJ9NM7zhd8BVq+q+XeaSNDxJrgVcB3g1zQeCkfNtmiHpikjybzQLhd+YpjHcZXfRzNvctZNgK5BkE+CJwL1och8LvLPvC58nuQbNBYFL2u01wNWq6sKZZej530jqrXadk2sBx4wPIZAkSepakhvSFEWXWzeuqn4++0Qr04dCaTWSfAu4Z1X9vt3eAvhcVf3FrDLYZEVapVkuWClJkrQSVfULYLelHpPk41X1kBlFWqnjaJYH+X27fXXgc8DMCqVV2mxU3AFU1e/bJR5mxjl4kiRJ0sapz0tsXK5QAmZaKK3SBUn2GG0kuS3wh1kG8AqeJEmStHHq81ytC5LsMVqzr4tCaZUOAT6a5H9o5g7eEHj4LANY4EmSJEnqm84LpZVq5wneGbgF67qs/7iq/jTLHBZ4kiRJ0sYpXQdYTF8KpZWqqkuSPLKq/gn4QVc57KIpSZIkbYSS3KuqPtd1jsUk+U5V7dV1jpVK8k/AVYEPAxeM9o+Gms4kgwWeJEmSNH+S3BF4GbAtzci90Tp4fW6uAvSjUFqNJF9aZHdV1d1nlsECT5IkSZo/SX4EPAc4CbhktL+qftNZqAn1oVAaKgs8SZIkaQ4l+XZV3b7rHBuTJDcAXgXcuKr2S7ILcIeqetesMrgOniRJkjSfvpTk9UnukGSP0VfXoSaR5AZJ3pXk6HZ7lyRP7DrXBN4LHAvcuN3+D+DZswzgFTxJkiRpDg15mGNb2L0HOLSqdktyFeC7VXXrjqMtKckJVXW7JN+tqtu0+06pqt1nlcFlEiRJkqQ5VFX7dJ3hCrheVX0kyQsBqmptkkuWe1IPXJDkz2gXkU+yN3DuLANY4EmSJElzKMm1gJcCd2l3HQ+8oqpmWnCsUueF0io9FzgS2CHJ14GtgANnGcACT5IkSZpP76ZZcPth7fZjaIY9PrizRJPrvFBapR8CnwQuBM4HPkUzD29mnIMnSZIkzaHF5n7Nej7YaiXZDHgGcG+aQumbwOFV9cdOgy0jyUeA84APtLseBVy7qh46qwxewZMkSZLm0x+S3KmqvgaXLXz+h44zTep9NIXSq9rtRwHvB2ZWKK3Srapql7HtLyX54SwDWOBJkiRJ8+mpwBHtXDyA3wGP7TDPSnReKK3SyUn2rqpvASS5PXDiLANY4EmSJEnz6XTgdcCOwLVpmpQ8EPh+d5Em1nmhtEq3Bb6R5L/a7W2AHyc5lWaJil2nHcACT5IkSZpP/w6cA5wMnN1tlBXrvFBapft0HcAmK5IkSdIcSvKDqrpV1zlWI8m2S91fVT+fVZah8QqeJEmSNJ++keTWVXVq10FWygJu9byCJ0mSJM2htinJzYCfAhcBod/DG3UlsMCTJEmS5tCGhjl6dWy+WeBJkiRJ0pzYpOsAkiRJkqQrhwWeJEmSJM0JCzxJkiRJmhMWeJIkSZI0JyzwJEmSJGlO/H+mbRs+vXfC4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = pd.Series(pipe.named_steps['gbm'].feature_importances_)\n",
    "importance.index = list(X_train.columns) + new_vars\n",
    "\n",
    "importance.sort_values(ascending=False).plot.bar(figsize=(15,5))\n",
    "plt.ylabel('Feature importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.054163,
     "end_time": "2021-01-23T12:18:49.311787",
     "exception": false,
     "start_time": "2021-01-23T12:18:49.257624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We see that some of the variables that we created are somewhat important for the prediction, like average_minerals, total_minerals, and total and average acidity.\n",
    "\n",
    "That is all folks!\n",
    "\n",
    "\n",
    "## References and further reading\n",
    "\n",
    "- [Feature-engine](https://feature-engine.readthedocs.io/en/latest/index.html), Python open-source library\n",
    "- [Python Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook)\n",
    "\n",
    "## Other Kaggle kernels featuring Feature-engine\n",
    "\n",
    "- [Feature selection for bank customer satisfaction prediction](https://www.kaggle.com/solegalli/feature-selection-with-feature-engine)\n",
    "- [Feature engineering and selection for house price prediction](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.05107,
     "end_time": "2021-01-23T12:18:49.413730",
     "exception": false,
     "start_time": "2021-01-23T12:18:49.362660",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "papermill": {
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   "end_time": "2021-01-23T12:18:49.573123",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-01-23T12:18:17.514853",
   "version": "2.1.0"
  },
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